How To Remove Percentage Of Amount
How to remove percentage of amount. Multiply the number by the percentage fraction. 15.6% = 15.6/100 = 0.156; Percentage calculation is one of the important topics that have to be taught to students from the beginning as this topic is directly or indirectly involved in different chapters. Click to show this example in the calculator above.
15% of $56.47 = $8.47 (which will be displayed under the % of start value box in the calculator) the total restaurant bill plus the tip is: To calculate the percentage of a number, we need to use a different formula such as: For example, if the gst rate is 5%, five divided by 100 equals 0.05. The amount of the tip is:
How to convert a decimal to a percentage. @osakagreg under tag_params.totalvalue = tag_params.totalvalue.replace('$', ''); The percentage difference is the variation in the value of a number or quantity over a while in terms of percentage. You have a 25 percent off coupon.
How to add or subtract percentages. A high school marching band has 12 flute players. The formula gives percentage change (or percentage difference) is: For example 25000+9% = 25000 x 1.09 = 27250.
% change =[(change in value) / original value] × 100 Find the percentage of the original or real number. Convert the percentage above to a decimal point by dividing it by 100. 575 + 92 = 667.
T o t a l = p a r t p e r c e n t × 100. Add alert(tag_params.totalvalue) it will be without the $.perhaps you need to move the %% to after. Add the increase to the original amount to show the new value. With the figures above you would do the following:
Multiply by 100 and add a percentage sign; Multiply the final number by 100 The total number of marbles 7 20 × 100 = 35. For example to calculate 9% of 25000, multiply 25000 by 9/100, that is, multiply 25000 by 0.09.
=a2*(1+b2) decrease a number in a cell by percent. C × b = amount to raise. To calculate a reverse percentage, convert your percentage by a decimal, divide your final amount by the decimal, then subtract the original amount from the final amount. Where x is the required percentage.
P/100 * number = x. Starting amount = 240 ( 100%) percentage = 12% divide 240 to 100 to get the 1% value 240 ÷ 100 = 2.40 one percent of 240 = 2.4 If we remove the % sign, then we need to express the above formulas as; Remove the percentage sign and divide by 100;
Percentage of red marbles = 20%. Percentage = (3 students/60 students)*100%. How to convert a percentage to a decimal. Number of red marbles = 7.
Subtract this number from the amount you paid to find the original price of the. Multiply the final number by 100. Here, we need to find the number of students absent. If you have some amount in a2 and you need to increase it by 10% in b2, here's your formula:
This helps students in calculating the overall percentage of marks. To decrease a number by a specific percentage, you can use a formula that multiplies the number by 1 minus the percentage. Find the percentage of the original or real number. In the example shown, the formula in cell e5 is:
Therefore, 5% of the students were absent. 0.876 = 0.876 * 100 = 87.6% You can solve a percentage change by dividing the whole value by the original value and then multiplying it by 100. If you want to find 15% of 1,367 start by working out 1%, move the decimal places one point to the right:
10/100 * 80 = x. If your calculator does not have a percent key and you want to add a percentage to a number multiply that number by 1 plus the percentage fraction. For a price or percentage increase: To subtract 30 percent from $27.90 as per the previous example, type 27.90 − 30 % = to get the answer of 19.53.
If you have a calculator with a percentage (%) key, you can subtract percentages. Part = 3, base = 60. 5.75 × 16 = 92 then: 575 ÷ 100 = 5.75 (1% of the initial amount) so:
Multiplying this by the number of percent you want to increase it by (b): Calculating a percentage of an amount the easiest way of calculating a percentage of an amount for me is to divide the amount by 100 to get the 1% value and then multiply that with the percentage amount. The percentage of the original number as given in the math problem is 40%. Let 10% of 80 = x.
Multiply the amount you paid by the decimal you found in step 2 to find the total amount of gst on the price of the item. The formula for solving a percentage change is the following: For a price or percentage decrease: To make the opposite and decrease the number by a percent, use the same formula as above but replace the plus sign with a minus:
Divide the result of the multiplication by the percentage. $56.47 + $8.47 = $64.94.
Finding the final amount given the original amount and a
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Back in 2007, with the help of a $1.7 million grant from the John Templeton Foundation, the folks at Johns Hopkins' Center for Talented Youth launched a new website called Cogito, devoted to math and science. The target market? Gifted young people who are comfortable with technology, want to meet other kids like them, and need a safe place on the web to do it.
Building up content and readership always takes time (we're almost celebrating our 4-year anniversary here at Gifted Exchange!), but two years later, Cogito is feeling pretty fleshed out. This week, there's an article on the winners of the International Math Olympiad, an interview with Preya Shah, a top-10 finisher at the Intel Science Talent Search this year, and links to interesting popular articles about math and science around the web.
I'd love to hear feedback from GE readers with kids who are Cogito regulars, and I encourage anyone who hasn't given it a look to check the site out.


Today’s children are growing up in a world very different from the one even 15 years ago. Seismic changes in the labor market mean that we are living and working in a knowledge-based economy—one that demands advanced literacy and Science, Technology, Engineering and Mathematics (STEM) skills, whether for application in the private or public sector. Today, information moves through media at lightning speeds and is accessible in ways that are unprecedented; technology has eliminated many jobs while changing and creating others, especially those involving mathematical and conceptual reasoning skills. One characteristic of these fast-growing segment of jobs is that the employee needs to be able to solve unstructured problems while working with others in teams. At the same time, migration and immigration rates around the world bring diversity to schools and neighborhoods. The exponential growth in interactions and information sharing from around the world means there is much to process, communicate, analyze and respond to in the everyday, across all settings. For a great majority of jobs, conceptual reasoning and technical writing skills are integral parts to the daily routine.
To prepare students for the changes in the way we live and work, and to be sure that our education system keeps pace with what it means to be mathematically literate and what it means to collaboratively problem solve, we need a different approach to daily teaching and learning. We need content-rich standards that will serve as a platform for advancing children’s 21st-century mathematical skills —their abstract reasoning, their collaboration skills, their ability to learn from peers and through technology, and their flexibility as a learner in a dynamic learning environment. Students need to be engaged in dialogue and learning experiences that allow complex topics and ideas to be explored from many angles and perspectives. They also need to learn how to think and solve problems for which there is no one solution—and learn mathematical skills along the way.
Students will only become successful in mathematics if they see mathematics as a whole, not as isolated skills and facts. As we develop mathematics curriculum based upon the standards, attention must be given to both content and process strands. Likewise, as teachers develop their instructional plans and their assessment techniques, they also must give attention to the integration of process and content. To do otherwise would produce students who have temporary knowledge and who are unable to apply mathematics in realistic settings. Curriculum, instruction, and assessment are intricately related and must be designed with this in mind. All three domains must address conceptual understanding, procedural fluency, and problem solving. If this is accomplished, school districts will produce students who will
- Make sense of problems and persevere in solving them
- Reason abstractly and quantitatively
- Construct viable arguments and critique the reasoning of others
- Model with mathematics
- Use appropriate tools strategically
- Attend to precision
- Look for and make use of structure
- Look for and express regularity in repeated reasoning
Dr. Satish Jagnandan
Director of STEAM


I enjoy a maths problem. A friend sends me one every Friday.
The type of question determines the best way to go about solving it. I like it best when I can write out the problem as an equation and work out the answer.
Some problems can only be solved (as far as I can see) by 'testing' different numbers. This was the case today and I managed to get the correct answer with a spreadsheet. But with more than one variable, this was a bit of a tedious process.
This is where BASIC programming comes into its own. As a teenager I would have tapped out a few lines of basic into my Vic-20 and had an answer very quickly.
I've tried to solve such problems more recently using C and my usual development environment with various degrees of success. Those used to writing CLI tools may argue that it's very easy to tap out a few lines of C, compile and run the app and get an output. But I've run into various problems, and no question about it - that's not as easy as this:
Today's problem was about sharing a box of chocolates (we don't know the total number) and one boy ends up with the same number of chocs as the percentage that his brother has. We need to know the smallest numbers for which this can be true. You can see here the two possible answers for numbers up to 100 chocs total. (20 chocs and 5 chocs (20%) out of a box of 25 and 50 chocs each (50%) from a box of 100)
I haven't written a BASIC prog like this for about 30 years, but it all flowed from the fingers very naturally. With a few more minutes of messing about I could have refined it and have a much smaller and more efficient programme.
Two points. First it's awesome to be working in such a small space. You can see that there are 3583 bytes to work in (yes, bytes. That's about 3.5k). The computer had a bit more on board but some is used by the screen. I bought an 8k ram pack for my Vic which seemed a massive amount of space at the time. (it brings to mind this challenge which involves writing a game in 140 characters!)
Second, this kind of programme could be written by a classroom of newbies in the first lesson. I'm well behind the Raspberry Pi project, but I'm not convinced it will inspire new coders like our 8-bit machines did. Switch it on with very little knowledge and within seconds you've filled the screen with your name. With a little more knowledge you'd made a small animation and after that made a simple game. My suggestion is a basic interpreter in the Applications folder of OSX and windows (and Rasp Pi). (As I have now!)


The appendix of McLachlan and Basford (1988) contains the listing of
FORTRAN programs for the fitting by maximum likelihood of normal mixture models
under a variety of experimental conditions.
Jones and McLachlan (1990) have
provided a FORTRAN subroutine
MGT (available at StatLib) for the (maximum likelihood) fitting of
mixtures of univariate normal (log normal) components to data that are grouped
into intervals and may also be truncated.
McLachlan, McLaren, and Matthews
(1995) have provided the FORTRAN subroutine NMFIT that fits a mixture of
g normal (lognormal) components for g=1 and g=2 in
turn to data in interval and truncated form, without need for the user to
specify the starting value for the vector of unknown parameters.
Moreover, if so required, it carries out the likelihood ratio test of
g=1 versus g=2 components by adopting a resampling
approach to the assessment of the null distribution of the likelihood ratio


The Benefits of Drawing
FACT: Drawing is a great way to learn the look and feel of shapes. When kids draw shapes, they learn that shapes are made of lines and curves.
TIP: On a sunny day, invite your child to draw a circle (sun) on a calendar or a piece of scrap paper to show that it’s SUNNY! Say, “Look, the sun’s a circle!”
GROWTH: Keep drawing shapes to build math skills! Show your child how to draw a house with a square (the house) and a triangle (the roof). Can they try, too?


Gabrielle De Micheli
Currently, I am focusing on the mathematical aspects of applied cryptography. My work lies at the intersection of Mathematics and Cryptography, with particular research interests discrete logarithm in finite fields, lattice-based cryptography, computational number theory and the Number Field Sieve algorithm. I am interested in both attacks and defenses with a particular interest in using mathematical techniques for obtaining a better understanding of the security properties of commonly used cryptographic primitives in real-world applications.
|September 2018 - current||PhD in Computer Science,Inria, University of Lorraine, Nancy, France|
|September 2016 - September 2018||PhD in Computer Science (transfer), University of Pennsylvania, Philadelphia, USA|
|May 2018||Master of Computer Science, University of Pennsylvania, Philadelphia, USA|
|October 2016||Master of Mathematics, EPFL, Lausanne, Switzerland|
|Sept 2015 - Jan 2016||Master Thesis, Imperial College, London, UK|
|July 2014||Bachelor of Mathematics, EPFL, Lausanne, Switzerland|
|Address||Office B250, LORIA
615, rue du Jardin Botanique
Fields of Interest
Applied Cryptography, Security, Algebra, Geometry (Riemannian Geometry), General Relativity
- A Tale of Three Signatures: practical attack of ECDSA with wNAF. Gabrielle De Micheli, Remi Piau, and Cecile Pierrot. [eprint]
- Characterizing overstretched NTRU attacks. Gabrielle De Micheli, Nadia Heninger, and Barak Shani. [eprint]
- CacheQuote: Efficiently Recovering Long-term Secrets of SGX EPID via Cache Attacks. Fergus Dall, Gabrielle De Micheli, Thomas Eisenbarth, Daniel Genkin, Nadia Heninger, Ahmad Moghimi, and Yuval Yarom. TCHES 2018. [Slides][video]
Workshops and invited talks
- CacheQuote: Efficiently Recovering Long-term Secrets of SGX EPID via Cache Attacks. Security Seminar, MIT, September 2018 [Slides]
- CacheQuote: Efficiently Recovering Long-term Secrets of SGX EPID via Cache Attacks. Security Seminar, University of Pennsylvania, September 2018 [Slides]
- Characterizing overstretched NTRU attacks. Mathcrypt, August 2018 [slides]
- 2013 Spring Semester (EPFL) - General Phyiscs II
- I play the piano.
- I enjoy mountaineering and traveling the world.


Mathematics is one of the key subjects for students to understand, it is the mother of all subjects. You can convert any conversion by online tools and apps and can learn subjects like Mathematics, this is quite great news for students and learners. For example, if you are dealing with a right-angle triangle then an online tool like the Sohcahtoa calculator is a reasonable option. You can find all the sides and the angles of the right-angle triangle in a matter of seconds.
This article illustrates the top 10 tools for learning concepts of Math.
Table of Contents
Convert any term of the right angle triangle in a matter of seconds with the online tool. Reading and writing a huge sum of terms in a matter of seconds by using Calculator-online.com. This makes the calculation just too simple and fast for the users to understand and find an answer.
2. All math:
The Sohcahtoa calculator by All Math is another impressive online tool. You are easily able to find all the sides and the angles of the right-angle triangle in a matter of seconds. The online tool can find all the values in the run as the user can solve the trigonometry question in no time.
3. Woot Math:
Interactive games and solutions to questions in a simple way are the trademark of Woot Math. Just enter any sides or the angles of the triangle online tool Woot Math. Just use the Sohcahtoa calculator and make it possible to draw the conclusion in a matter of seconds.
4. Calculators black:
This makes it possible for the users to solve the equestrian and draw the graph in a matter of seconds. The interface of the online tool and calculator is easy to understand for the users. Enter the measurements and click the calculator button to find any value.
The Sohcahtoa calculator by ECalculator provides us with another platform to convert any quantity. The standard form or point-slope can be just too difficult for us to understand if we are doing it for the first time. ECalculator makes the measurement just too easy for us.
6. Microsoft Math:
Microsoft Math is an interesting way to learn Math and various concepts. Learn the difficult concept in the easiest way by using an online tool like the Sohcahtoa calculator. Learn the derivations and integration in the easiest way. You just need to enter the world of Microsoft Math and learn various concepts of Math.
GeoGebra is one of the most Interactive Math tools. Apply this to learn the concepts of algebra, geometry, statistics, graphs, and calculus. You may wonder how to find the equation of a line. Find the equation of the line by using the GeoGebra online Sohcahtoa calculator. You predict the equation of a line and its coordinates by the online tools by extending the Cartesian coordinates.
Also read: How to transfer files from mobile to laptop?
8. Learning point:
Learning Point is one of the interactive apps and calculators. It has separate user interfaces for Algebra, Geometry, Trigonometry, Probability, and Calculus for students. Implement the concepts in the various concepts of math on online tools and apps.
9. Function plotter:
The Functions Plotter app is a simple app and there is nothing complex about it. Nothing is complicated about the app, this app assists in learning the basic concepts of Mathematics and algebra. Easy to install this app on Windows 8 and 10 without any difficulty.
The iMath is specially planned and prepared for the Kids to learn the skills of math. It assists students to perform better in a class by learning the basic concepts of math. The app is specially prepared for kids under 12 who can learn a lot from this app. The interface is really simple and easy to use for the kids to solve various problems. You can teach the students to learn from the Sohcahtoa calculator and iMath.
You just need to enter the values in the online tool, and it would easily be able to find the answer to any query. You can learn the various concepts of math through interactive apps and tools.


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A gaggle at Politecnico di Milano has developed an digital circuit in a position to resolve a system of linear equations in a single operation within the timescale of some tens of nanoseconds. The efficiency of this new circuit is superior not solely to classical digital computer systems, but in addition to quantum computer systems. It will likely be quickly potential to develop a brand new technology of computing accelerators that can revolutionize the expertise of synthetic intelligence.
Fixing a system of linear equations means discovering the unknown vector X which satisfies the equation Ax = b, the place A is a matrix of coefficients and b is a identified vector. To resolve this downside, a traditional digital laptop executes an algorithm that takes a number of operations, thus translating into appreciable time and power consumption.
The brand new circuit, which has been developed within the body of the ERC European venture Resistive Change Computing Past CMOS (RESCUE), solves techniques of linear equations (Ax=b) because of an revolutionary methodology of in-memory computing, the place the coefficients of matrix A are saved in a particular gadget referred to as a memristor. The memristor is ready to retailer analogue values, so a memristor matrix can bodily map a coefficient matrix A throughout the circuit, thus strongly accelerating the computation.
The memristor circuit has been examined and validated on a large set of algebraic issues, such because the rating of web web sites and the answer of difficult differential equations together with the Schrödinger equation for the computation of the quantum wavefunction for an electron. All these issues are solved in a single operation.
These outcomes have been revealed within the Proceedings of the Nationwide Academy of Science.
Reminiscence-processing unit might deliver memristors to the lots
Zhong Solar et al. Fixing matrix equations in a single step with cross-point resistive arrays, Proceedings of the Nationwide Academy of Sciences (2019). DOI: 10.1073/pnas.1815682116


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Tomorrow the Universe Requires You to Eat Pie
Tomorrow is 3/14 and it will be time to eat pie. See, if you take the circumference of this bright and delicious Rhubarb Lattice Pie with Cardamom and Orange and divide it by its diameter, the result will be approximately 3.14.
This will be the case with any of our top 20 fruit pies.
You’re not the only one blown away by this. People across the globe will celebrate the mind-blogging mathematical constant pi tomorrow, because no matter how anyone feels about geometry (it’s awesome), every single person loves pie (I already did the math).
It will also be Albert Einstein’s birthday, so you might as well have some cake too.
(photograph by Tina Rupp)


Since a number of people asked, here are the slides from my ICM talk yesterday. I have also posted them on my preprints page. I believe the talk was recorded and the video will presumably be available from the ICM webpage. Alternatively, you can read my ICM proceedings paper for a longer version.
Category Archives: mathematics: research
Somewhat belatedly, here’s the expository paper I wrote for the ICM Proceedings: a short overview of my work with Malabika Pramanik, Vincent Chan and Kyle Hambrook on harmonic analytic estimates for singular measures supported on fractal sets.
The connection between Fourier-analytic properties of measures and geometric characteristics of their supports has long been a major theme in Euclidean harmonic analysis. This includes classic estimates on singular and oscillatory integrals associated with surface measures on manifolds, with ranges of exponents depending on geometric issues such as dimension, smoothness and curvature.
In the last few years, much of my research has focused on developing a similar theory for fractal measures supported on sets of possibly non-integer dimension. This includes the case of ambient dimension 1, where there are no non-trivial lower-dimensional submanifolds but many interesting fractal sets. The common thread running through this work is that, from the point of view of harmonic analysis, “randomness” for fractals is often a useful analogue of curvature for manifolds. Thus, “random” fractals (constructed through partially randomized procedures) tend to behave like curved manifolds such as spheres, whereas fractals exhibiting arithmetic structure (for instance, the middle-thirds Cantor set) behave like flat surfaces. There is a clear connection, at least on the level of ideas if not specific results, to additive combinatorics, where various notions of “randomness” and “arithmetic structure” in sets of integers play a key role.
The paper discusses three specific questions that I have worked on: restriction estimates, differentiation estimates, and Szemeredi-type results. I’ve also mentioned some open problems. At this point, I feel like we’re only started to scratch the surface here; there is much more left to do, for example optimizing the exponents in some of the estimates I’ve mentioned and, perhaps more importantly, figuring out what properties of fractal measures determine such exponents.
Very briefly, the question we consider is the following. Let be a closed set of Hausdorff dimension . Given a system of matrices for some $m \geq n$, must contain a “non-trivial” k-point configuration
for some and ?
In general, the answer is no, even when . For instance, Keleti has constructed 1-dimensional subsets of that do not contain a similar copy of any given triple of points (in fact, his construction can avoid all similar copies of all such triples from a given sequence), as well as 1-dimensional subsets of that do not contain any non-trivial “parallelograms” . In , given any three distinct points , Maga has constructed examples of sets of dimension 2 that do not contain any similar copy of the triangle ; he also constructed sets of full dimension in , for any , that do not contain non-trivial parallelograms.
Additive combinatorics suggests, however, that sets that are “random” in an appropriate sense should he better behaved in that regard. Along these lines, Malabika Pramanik and I proved in an earlier paper that if has dimension close enough to 1, and if it also supports a measure obeying appropriate dimensionality and Fourier decay estimates, then must contain a non-trivial 3-term arithmetic progression. The same proof applies to any other configuration , with the dimension bound depending on the choice of configuration.
This paper gives a multidimensional analogue of that result. We define, via conditions on the matrices , a class of configurations that can be controlled by Fourier-analytic estimates. (Roughly, they must have enough degrees of freedom, and they must be “non-degenerate” in an appropriate sense.) For such , if has dimension close enough to , and if it supports a measure with dimensionality and Fourier decay conditions similar to those in my paper with Pramanik, then must indeed contain a non-trivial configuration as in (1).
The main new difficulty is dealing with the complicated geometry of the problem. There’s a lot of linear algebra, multiple coordinate systems, multilinear forms, and a lot of estimates on integrals where the integrand decays at different rates in different directions. At one point, we were actually using a partition of unity similar to those I remembered from my work in multiparticle scattering theory a very long time ago. That didn’t make it into the final version, though – we found a better way.
I won’t try to state the conditions on here – they’re somewhat complicated and you’ll have to download the paper for that – but I’ll mention a few special cases of our theorem.
- Triangles in . Let be three distinct points in the plane. Suppose that satisfies the assumptions of our main theorem. Then must contain three distinct points such that the triangle is a similar (possibly rotated) copy of the triangle .
- Colinear triples in . Let be three distinct colinear points in . Assume that satisfies the assumptions of our main theorem. Then must contain three distinct points that form a similar image of the triple .
- Parallelograms in . Assume that satisfies the assumptions of our main theorem. Then contains a parallelogram , where the four points are all distinct.
Matt Bond, Josh Zahl and I have just completed a new paper “Quantitative visibility estimates for unrectifiable sets in the plane,” now available on the arXiv. This post is an informal introduction to the paper; for more details, you will need to download the actual article.
There are several questions known as “visibility problems”, and the one we address is the following. We are given a compact set in the plane, and a point not in . Define to be the radial projection from :
Then is the set of angles at which is visible from . Our “visibility problem” is then to estimate the size of , or equivalently, the proportion of the part of the field of vision that takes up for an observer situated at .
One class of sets that we will study is 1-dimensional unrectifiable self-similar sets. A good example to keep in mind is the “4-corner set,” constructed via a Cantor iteration as follows. Start with a square, divide in into 16 congruent squares, and keep the 4 small squares at the corners, discarding the rest. Repeat the same procedure for each of the 4 small surviving squares, then iterate the construction. The first and second stage of the iteration are shown below.
We will use for the -th iteration of this set, and for the Cantor set .
What can we say about the visibility of from points in the plane? We will assume that , so as to avoid trivial debates over whether a point is visible from itself. We will be asking this question in terms of the size of , as expressed in terms of its Lebesgue measure and/or Hausdorff dimension.
In case there’s anyone here who’s interested in the Favard length problem, I have just finished an expository paper written for the proceedings of the 2012 Abel Symposium. There has been a good deal recent progress on the subject in recent years, starting with this 2008 paper by Nazarov, Peres and Volberg, through my paper with Zhai, two papers by Bond and Volberg, and most recently my paper with Bond and Volberg. This exposition focuses especially on the number-theoretic aspects of the question for rational product sets, developed mostly in the BLV paper, although some of it goes back to the earlier papers. You can think of it as BLV-lite if you wish.
I have tried to keep the exposition as simple as possible, omitting many of the technicalities and focusing on examples where we can deal with just one number-theoretic issue at a time (as opposed to BLV, where we must combine the different methods together). I’ve also added a good deal of discussion and commentary. This makes the paper a bit more verbose than what I’m used to, but most of this was written in response to questions that I have actually been asked, so I hope that this will be a useful companion paper to BLV and the other references. Also, I did have a deadline for this, so a couple of things (notably the “Poisson lemma” in Section 3.1) got short shrift, and I probably would have found a few more typos and other such if I’d had more time to chase them. Oh well.
There are a couple of new things at the end of the paper. One is Conjecture 4.6. Matt Bond and I came up with this while trying to figure out whether the assumption on the cardinalities of product sets in BLV can be dropped. If the conjecture turns out to be true, than we can indeed drop that assumption. We have some supporting evidence for various special cases, but we don’t know how to prove it in general.
The second part that has not been published previously concerns “random 4-corner sets”. Peres and Solomyak (Pacific J. Math. 2002) proved that for a randomized version of the 4-corner set construction, the expected Favard length asymptotics is in fact C/n. This is a very nice geometric argument, but I found the original proof quite hard to read, so I reworked and simplified it some time ago. This is included here in Section 5.
The paper can be downloaded here. It will also be posted on the arXiv, if I can figure out how to post LATEX files with pictures.
The restriction phenomenon in harmonic analysis is best known for surface measures on manifolds. A classical example is the unit sphere, where on the one hand we have the Stein-Tomas restriction theorem for densities on the sphere, and on the other hand, Stein’s restriction conjecture for densities remains open. (Partial intermediate results are also available, but that is a longer story that will have to wait for another time.)
However, restriction estimates can also be proved for fractal sets. Continue reading
One of the things I have been working on in the last few years is the Favard length problem. The question is to estimate the average length of a 1-dimensional projection of (a finite iteration of) a 1-dimensional self-similar Cantor set in the plane. My work with Zhai, and especially with Bond and Volberg, has pointed to connections with classical questions in number theory, including tilings of the integers, diophantine approximation of logarithms of algebraic numbers, and vanishing sums of roots of unity.
If you would like to find out a little bit more about this, but don’t necessarily feel like reading long technical papers that rely on several other long technical papers, then this very short expository note (3 pages plus short bibliography) might be for you. It was written for the CMS Notes and has about the length they require. I found myself wanting to make it longer, if only to include more references to the history and context of the problem. (I never even mentioned Comte de Buffon.) I might write a more substantial expository paper when I have the time.


don't tell him he could have gone 49 seconds faster!)
It's all part of the mathematical study of "extreme values" that helps determine upper limits. A quote from the story:
"For a lot of athletes it is probably depressing when they are confronted with our extreme values," Einmahl told the German news agency dpa. "But this is a very serious study - the extreme theory as a part of mathematics and statistics is an accepted science."It's all math, so there is certainly the chance of a breakout performance. I wonder what the 100-mile limit would be? Perhaps this will be one of Professor Einmahl's graduates thesis.
[Thanks to The Final Sprint for pointing me to this story]


3 edition of Mathematical modelling for information technology found in the catalog.
Mathematical modelling for information technology
Includes bibliographies and index.
|Statement||editors, A.O. Moscardini, and E.H. Robson.|
|Series||Mathematics and its applications. Statistics and operational research, Ellis Horwood series in mathematics and its applications.|
|Contributions||Moscardini, A. O., Robson, E. H.|
|LC Classifications||TK5105.5 .M39 1987|
|The Physical Object|
|Pagination||214 p. :|
|Number of Pages||214|
|LC Control Number||87032039|
Offers information necessary for the development of mathematical models and numerical techniques to solve specific drying problems. The book addresses difficult issues involved with the drying equations of numerical analysis, including mesh generation, discretization strategies, the nonlinear equati. This book provides a thorough introduction to the challenge of applying mathematics in real-world scenarios. Modelling tasks rarely involve well-defined categories, and they often require.
Dr. Alexiou Athanasios. Honorary Admin Rector and Faculty Staff of the under registration Novel Global Community Education Foundation. Dr. Alexiou Athanasios is a Mathematician with a PhD in Bioinformatics Algorithms and Mathematical Modeling in Biology and a two-years PostDoc Research Fellowship in Neuroinformatics Applications. "Engaging, elegantly written." — Applied Mathematical ModellingMathematical modelling is a highly useful methodology designed to enable mathematicians, physicists and other scientists to formulate equations from a given nonmathematical situation. In this elegantly written volume, a distinguished theoretical chemist and engineer sets down helpful rules not only for setting up Reviews: 1.
mathematical concepts are very important to know for students if they want to do something unique in the field of research and development. One of the most important and significant concept of mathematics is mathematical modelling. Mathematical modelling is a concept which used in natural science, engineering disciple and in social science too. Journal of Mathematical Models in Engineering (MME) ISSN (Print) , ISSN (Online) publishes mathematical results which have relevance to engineering science and technology. Formal descriptions of mathematical models related to engineering problems, as well as results related to engineering applications are equally encouraged. Established in and .
Italian social democracy yesterday and today.
effects of labour legislation on personnel practices in the EU
Agriculture Dictionary in Six Languages
Way-marks in the life of a wanderer
Biggles hits the trail
OFHEO risk-based capital stress test for Fannie Mae and Freddie Mac
Investor information needs and the annual report
Numerical simulation of liquefaction
Alternative Strategies to Improve and Expand the Delivery of Vocational Education in Small, Rural, and/or Isolated Secondary Schools in Hawaii
Surface Modification of Polyester by Alkaline Treatments (Textile Progress)
“Mathematical modelling is a product of several different contributors, all from Finnish universities.
The result is a text used by over 2, students. This is an excellent book, written with a myriad of interesting and very practical problems and applications. The book combines classical approaches to modelling with novel areas such as soft computing methods, inverse problems, and model uncertainty.
Attention is also paid to the interaction between models, data and the use of mathematical software. 28 rows Heat Transfer - Mathematical Modelling, Numerical Methods and Information Cited by: An important resource that provides an overview of mathematical modelling Mathematical Modelling offers a comprehensive guide to both analytical and computational aspects of mathematical modelling that encompasses a wide range of subjects.
The authors provide an overview of the basic concepts of Mathematical modelling for information technology book modelling and review the relevant topics from.
Mathematical Modelling in Science and Technology The Fourth International Conference, Zurich, Switzerland, August be checked theoretically, whereas the other models depend solely on empirical control.
This forces the mathematical-modelling community to reconsider its relations to the public. The models analyze the interaction. This book constitutes the refereed proceedings fo the 14th International Scientific Conference on Information Technologies and Mathematical Modeling, named after A.
Terpugov, ITMMheld in Anzhero-Sudzhensk, Russia, in November The 35. His research interests include mathematical control theory, distributed parameter systems, mathematical modelling and simulation, development and use of information technology to support learning.
Currently he is leading a nationwide network in Finland on teaching mathematical modelling on the Internet/5(18). “Mathematical modelling is a product of several different contributors, all from Finnish universities.
The result is a text used by over 2, students. This is an excellent book, written with a myriad of interesting and very practical problems and : $ Each Chapter Of The Book Deals With Mathematical Modelling Through One Or More Specified Techniques.
Thus There Are Chapters On Mathematical Modelling Through Algebra, Geometry, Trigonometry And Calculus, Through Ordinary Differential Equations Of First And Second Order, Through Systems Of Differential Equations, Through Difference Equations, Through Partial 5/5(4).
: Information Technologies and Mathematical Modelling. Queueing Theory and Applications (Communications in Computer and Information Science) (): Dudin, Alexander, Nazarov, Anatoly, Kirpichnikov, Alexander: BooksFormat: Paperback.
Optimum Fin Profile under Dry and Wet Surface Conditions, Heat Transfer - Mathematical Modelling, Numerical Methods and Information Technology, Aziz Belmiloudi, IntechOpen, DOI: / Available from: O IntechOpen readers like this topic.
Help us write another book on this subject and reach those readers Author: Balaram Kundu, Somchai Wongwises. in their areas ofwork. The book was about halfofthe complete course, the rest being statistical modelling. Everything I write in this book from now on is addressed to the reader on the assumption that he or she has a similar background, and similar or broader interests.
I assume, in other words, that you are not a mathematician, physi. Mathematical modelling for information technology: telecommunication transmission, reception, and security. Purchase Mathematical Modelling in Science and Technology - 1st Edition.
Print Book & E-Book. ISBNBook Edition: 1. International experts contribute their knowledge and experience by providing analysis, insight and comment whilst tackling large and complex problems by applying mathematical modelling.
This book covers the proceedings from the Twelfth International Conference on the Teaching of Mathematical Modelling and Applications. This book constitutes the proceedings of the 18th International Conference on Information Technologies and Mathematical Modelling, ITMMnamed after A.F.
Terpugov, held in Saratov, Russia, in June The 25 full papers presented in this volume were carefully reviewed and selected from 72 submissions. Applied Mathematical Modelling is primarily interested in papers developing increased insights into real-world problems through novel mathematical modelling, novel applications or a combination of these.
Papers employing existing numerical techniques must demonstrate sufficient novelty in the solution of practical problems. Book Description. Written by experts from all over the world, the book comprises the latest applications of mathematical and models in food engineering and fermentation.
It provides the fundamentals on statistical methods to solve standard problems associated with food engineering and fermentation technology. Mathematical modeling problems are often classified into black box or white box models, according to how much a priori information on the system is available.
A black-box model is a system of which there is no a priori information available. A white-box model (also called glass box or clear box) is a system where all necessary information is available.
Purchase Mathematical Modelling - 1st Edition. Print Book & E-Book. ISBN. The overall goal of Modelling and Applications in Mathematics Education is to provide a comprehensive overview of the state-of-the-art in the field of modelling and applications in mathematics education.
Key issues are dealt with, among which are the following: Epistemology and the relationships between mathematics and the "rest of the world"; the meaning of .Information Technologies and Mathematical Modelling.
Queueing Theory and Applications 17th International Conference, ITMMNamed After A.F. Terpugov, and 12th Workshop on Retrial Queues and Related Topics, WRQTomsk, Russia, September, Selected Papers Communications in Computer and Information Science by Alexander Dudin Editor .Creating a mathematical model: • We are given a word problem • Determine what question we are to answer • Assign variables to quantities in the problem so that you can answer the question using these variables • Derive mathematical equations containing these variables • Use these equations to find the values of these variablesFile Size: KB.


Mathematics – is it really difficult?
Mathematics is absolutely necessary in everyday life and it is essential in so many other areas of our lives.
Mathematics is usually difficult for many students and in this blog, we would explore some of the possible reasons.
As a teacher for nearly two decades, I have come to realize that many students develop a dislike for Mathematics because of an ingrained perception that the subject area is difficult. This perception may have been passed on indirectly from parent to child. This perceived dislike for the subject, more often than not would have begun by age seven or so for many. Students transition from Second Year, (age 6 or so) to Standard One (age 7) and are required to grasp more difficult concepts. If improperly handled, misconceptions develop and so does the dislike. Parents, because of how they would have been taught, would have also developed a dislike for the subject area. I have heard parents say, “Miss, I never liked Maths in school” or “Miss, I was never good in Mathematics, nah.”
Another reason that many students find the subject area difficult is the teacher’s method, style of teaching and attitude towards Mathematics. Students can easily recognize when a teacher does not particularly like any subject area and that includes Mathematics.
The teacher’s approach to the teaching of Mathematics should include Concrete, Pictorial and Abstract or Symbolic stages. One or two of these aspects are sometimes missing. However, if this approach is used, it almost guarantees a deeper understanding of mathematical concepts and the eventual liking of the Mathematics.
The concrete stage in the teaching of Mathematics requires students ‘to do’, manipulate or count actual objects, for example, bananas. Real life experiences allow students to be part of the problem they are attempting to solve.
As students master the skill at the concrete stage, they move to the pictorial stage. At this stage, students now use pictures of the objects; which represent the actual objects and students should now be able to make mental connections between the pictures and the actual objects that they manipulated. Making these mental connections, help students grasp the concept presented to them.
Finally, students progress to the abstract or symbolic stage where the objects are represented by symbols.
Students may face challenges or difficulties, if they did not grasp fully the concepts during the first two stages: concrete and pictorial. The longer these challenges or difficulties remain unresolved, the child’s dislike for Mathematics would deepen.
Students must also view Mathematics as necessary for living in the real world. Teachers and parents can of course, incorporate Mathematics in as many areas as possible. Mathematical skills are essential in cooking as when measuring ingredients for a favourite recipe; or when using their knowledge of time so as to remove a cake from the oven; a child’s knowledge of perimeter would be useful when determining the material needed for fencing property. An awareness of the practicality of these skills surely can determine a student’s interest in the subject area.
If you want to learn more about how you can assist your son and daughter, feel free to subscribe to our mailing list. You can also visit our website at www.boldprintbookshop.com or follow us on Facebook @boldprintbookshop.


So, with that and Valentine's Day in mind I made a new Operations & Algebra functions math pack.
- Make 10 Match - A matching game wherein kids practice addition equations that equal 10. This will assist in memorization and speed of 10 facts. It also includes a worksheet for the entire class to use as practice.
- Number Love Road - A file folder game for students to practice adding 3 digits, encouraging them to initially find facts that equal 10 when posible. This also includes a practice worksheet.
- Lost and Found Addition - An algebra practice addition game. This game has students practice algebraic addition - where they identify missing addends. A practice worksheet is included.
- Missing Link - Another algebra practice game where students play a card game focusing on finding the missing subtrahend of math problems. The practice worksheet has students finding the "missing links" that will bring the little Number Love Bandit back into lock-up. ;D
Please head over to one of my stores to check it out. It is seriously cute and filled with some really great learning tools.
While you are over there, check out the other math games ideal for February. Both sets of February math games will be out in my classroom within a few short days. (WHERE did January go?!)
This is a 58 page math pack filled with games and practice/assessment pages. Although the pack contains February themes - it provides great practice for anytime of the school year. It includes
- Snuggy Buggy Fact Family - A Fact Family Card Game for sums 10 to 17. This game allows students plenty of opportunity to practice larger number math facts. Students identify the missing component in fact families by using the colorful and cute Snuggy Buggy playing cards. Additionally, there are several worksheets that allow students to practice fact families.
- Presidential Podium Facts - In this file folder game, students practice higher-level addition and subtraction as well as algebraic thinking. This file includes several addition and subtraction mats, 6 sets of of number cards and several worksheets for practice and assessment.
- Special Delivery is a file folder game where students practice ordering numbers from greatest to least and least to greatest. This file also includes worksheets for students to practice the skill and/or provide an opportunity for assessment.
- Stars and Stripes Equations is a flag-making project that includes a mini-lesson, all parts to make a flag, corresponding math problems and a "write a math problem" activity.
Groundhog Day is already around the bend?! Really?
This year is going so fast. The better the class...the faster the year. On the plan next week is a whole of of assessments. And some groundhog fun. :D
We've been working a lot on descriptive verbs and adjectives. Groundhog Phil's Fascinating World is another chance to practice descriptive language. And it's adorable!
This is a pic of my bulletin board from last year
I have a whole lot on the plans for February. What a busy month. Lulu the Love Bird and the Presidential Fun bundle include writing lessons, reading and social studies activities and adorable crafts. I'll be writing more about them in a later post.
You can pick any of my items up at either of my stores.
Congrats Suzy Q! Check your email.


Abstract: I propose an alternative two-step generalized method of moment (GMM) estimator when an exactly-identified underlying parameter is available. The resulting estimator does not have the higher-order asymptotic bias arising from the choice of a preliminary weighting matrix.
"Difference-in-differences Estimator of Quantile Treatment Effect on the Treated"
(with Jefferey M. Wooldridge)
Abstract: We propose a difference-in-differences (DID) estimator of the quantile treatment effect on the treated (QTT). The new distributional DID assumption has two distinctive features. Firstly, it is necessary and sufficient for the the usual DID assumption (with covariates) to hold for any strictly increasing transformation of the outcome variable. Secondly, it is partially testable through a shape restriction. We formalize that the new condition requires net changes of untreated outcome density to be common. It would be misleading to view this condition as a mandate to partition the population into two subgroups with specific characteristics. We establish uniform consistency and weak convergence of the proposed estimator of QTT and related functions. The estimators and simultaneous confidence bands remain valid even for discrete outcome variables. As an empirical application, the distributional impact of the earned income tax credit on birth weight is investigated.
PDF file: (download)
"Linearized GMM Estimator"
Abstract: Given a standard moment condition, I show that there exists an underlying exactly-identified parameter. Exploiting the underlying parameter, an alternative GMM estimator based on a linearized moment function is proposed. The linearized moment function is linear in the original parameter. The asymptotic properties of the linearized GMM estimator are investigated. In particular, the higher-order asymptotic bias is derived.
"Short Panel Data Quantile Regression Model with Flexible Correlated Effects: A Partially Linear Model Robust to Misspecification"
Abstract: I propose an alternative linear model for short panel data quantile regression. The model assumes a nonparametric correlated effect (CE) that is τ-quantile-specific and time-invariant. The resulting partially linear model is robust to misspecification, and it is characterized as a best linear approximation to the truth under a generalized correlated random effect assumption. At the cost of modeling the individual heterogeneity, the new model is free of incidental parameters, and it does not restrict within-group dependence of idiosyncratic errors at all. The modeled heterogeneity is still well-aligned with the fixed effect approach in the linear mean regression model. For estimation, sieve-approximated CE is regularized by non-convex penalization which enjoys the oracle property against ultra-high dimensionality. Unpenalized sieve estimation is also available. As an empirical application, the proposed method is applied to estimate the distributional effect of smoking on birth weights.
PDF file: (download)
STATA ado file: (download)
"Asymptotic Efficiency of Joint Estimator relative to Two-stage Estimator under Misspecified Likelihoods"
Abstract: The two-stage estimator is often more tractable when there are nuisance parameters that can be separately estimated and plugged into an objective function. The joint estimator tends to bear the higher computational cost since it estimates all parameters in one stage by optimizing the sum of objective functions used in the two stages. It is well-known that joint estimator is asymptotically more efficient than two-stage estimator if the objective function is the true log-likelihood. When the objective function is not the true log-likelihood, I show that the relative asymptotic efficiency of joint estimator still holds under a finite number of moment conditions that are testable. The implications of the main result on models based on quasi-limited information likelihoods are discussed.


The lottery is a game of probability and luck. Toto is gambling which involves huge variance. One can either lose a hundred bucks or win up to 1000 bucks. So if you want to earn money, then the best way to do so is by placing bets. It takes work to win money online. You can’t just throw away your money when the chance of winning is only about 1%. So it becomes important to understand the concept of the online Toto lottery to win huge. If you go in-depth, you will understand why playing is difficult,especially when you don’t know how to bid a number. However, with several disadvantages, online lottery games have certain advantages. Click here to know more.
How to win at TOTO Macau
In Toto, choose from a minimum of 6 numbers to a maximum of 12 numbers between 1 and 49. Below are the types of bets:
Quick pick: In this, the system generates 6 number combinations randomly between 1 and 49.
Ordinary Bet: In this, the person can select six numbers from 1 to 49.
System 7 to 12: The player can select at least 12 numbers from 1 to 49. With more numbers, the chance of winning increases.
iTOTO:In this system randomly generates 12 numbers from 1 to 49. However, the prize given in this bet is quite huge. One can win up to thousand bucks.
System Roll: The player can select five numbers from 1 to 49.
Do you need help to win at Toto Macau?
Mostly the people who are betting online are interested in putting more effortinto getting the reward instead of attaining the cost. With a high betting type, the chance of winning increases. The probability and odds are mostly derived mathematically. There are various lottery games in which one can participate. So if you want to win a lottery game,understand the concept first, which includes combining numbers using permutation and combination.
Advantages of online lottery
The game of chance comes with many benefits, which include:
Ad hoc payments:If the player does not have the money, there is no need to play. They can withdraw their name at any time. Moreover, a person who plays often requires less discipline and planning investment schemes.
Affordability:People can play the game, even with one dollar. It is optional to spend a huge amount of money. Some websites even provide a welcome bonus with which a person can start playing.
Easy:Anyone can play from anywhere. This is the best part about online lottery games. The user can buy the ticket online easily.
Fun: Most people indulge in online gaming and betting. This is because it can reduce stress and make life much more entertaining.
In conclusion, we have seen how to win at Toto Macau. To know more about the game, click here.


A while back when our Relief Society was in high gear doing these extremely high frequency enrichment activities, I casually mentioned to my wife how it would be cool to have a session on how to teach math to your kids. Word got back to the person scheduling these sessions, and somehow I was signed up to teach a session. Personal time constraints and a fundamental lack of experience in the area prevented me from pulling anything together, and the thing was ultimately shelved.
But one of the things I did do was go to the library to see what kind of books I could muster up on the subject. In the search I came across the book, Young Children Reinvent Arithmetic, Implications of Piaget's Theory. Its a pretty dense book really meant for classroom teachers and leans heavily on Paiget's theories on how children learn. It has an interesting premise, if I remember correctly, Piaget wanted to discover how human beings discovered knowledge in the first place, and to do so, he studied how children acquired knowledge as they aged.
But what hooked me in to this idea was chapter 4 entitled: "Autonomy: The Aim of Education for Piaget" claiming that obtaining knowledge really is about a child's development toward autonomy.
Here's a quote:
"An unusual example of moral autonomy is the struggle of Martin Luther King, Jr. for civil rights. King was autonomous enough to take relevant factors into account and to conclude that the laws discriminating against African Americans were unjust and immoral. Convinced of the need to make justice a reality he worked to end the discriminatory laws, in spite of the police, jails, dogs, water hoses, and threats of assassination used to stop to stop him. Morally autonomous people cannot be manipulated with reward and punishment."
The chapter continues with a discussion on how schools are geared to largely manipulate especially in mathematics. Math is pretty unique because there are truly right and wrong answers usually, but there are not necessarily wrong or right ways of coming up with the answer. The point of the book is that its not necessarily as important to get the answer right as it is in the struggle to get to the answer autonomously. To come up with internal methods to find it, and to learn to be sure enough in the answer to defend it to others. If in the process of defending your answer, you discover our methods are wrong, room is provided to reassess and fix the mistakes until the right answer is found.
The danger in our current teaching approach in mathematics is when the student views the teacher as the ultimate source knowledge, rather than learning to search from within. In reality, mathematics is intrinsically true. Numbers and equations that model nature around us existed before humans did. We came up with symbols and ways to represent them, but addition, subtraction, numbers were not invented they were discovered. The process of learning math, in Piaget's view, is to allow a child to go through the process of mathematical discovery in their own way working collaboratively with other children in the classroom using the teacher more as a guide and less as source for truth.
So, obviously this hooked me in. Any book that can use Martin Luther King Jr. to explain mathematical teaching is going to hook me in.
Well, with that book in my head, recently, I read about why programmers (like me) should learn math.
Here are some points:
"In fact, I don't think you need to know anything, as long as you can stay alive somehow."
On math education:
"They teach math all wrong in school. Way, WAY wrong. If you teach yourself math the right way, you'll learn faster, remember it longer, and it'll be much more valuable to you as a programmer."
"Math is... ummm, please don't tell anyone I said this; I'll never get invited to another party as long as I live. But math, well... I'd better whisper this, so listen up: (it's actually kinda fun.)"
How you should learn math:
"The right way to learn math is to ignore the actual algorithms and proofs, for the most part, and to start by learning a little bit about all the techniques: their names, what they're useful for, approximately how they're computed, how long they've been around, (sometimes) who invented them, what their limitations are, and what they're related to. Think of it as a Liberal Arts degree in mathematics.
Why? Because the first step to applying mathematics is problem identification. If you have a problem to solve, and you have no idea where to start, it could take you a long time to figure it out. But if you know it's a differentiation problem, or a convex optimization problem, or a boolean logic problem, then you at least know where to start looking for the solution."
I think this is more true in high school than in early grades because you need to have the foundations of numbers, addition, subtraction, multiplication, etc. down solid. You can't understand algebra or geometry if you don't know how numbers relate to one another (the network of numbers as the Piaget book describes it). And you really need to know algebra and geometry.
Finally, I read Outliers and this quote:
"We sometimes think of being good at mathematics as an innate ability. You either have 'it' or you don't. But to Schoenfeld, it's not so much ability as attitude. You master mathematics if you are willing to try. That's what Schoenfeld attempts to teach his students. Success is a function of persistence and doggedness and the willingness to work hard for twenty-two minutes to make sense of something that most people would give up on after thirty seconds."
The point is that people who don't get math, don't get it because they give up too early. Math, with its heavy emphasis on confusing-at-first symbols and equations is on the surface one of the most intimidating subjects around. But if you approach it systematically with focus and persistence, it is really not that hard. But it takes a certain kind of mindset, the willingness not to give up, and patient practice. The kind of attributes that translate really nicely in all kinds of other areas.
Summing all of this information together, I want to see my children really getting the basics principles deeply embedded. They need to not just know their math and subtraction tables, they need to understand deeply how numbers relate to each other.
Its why I think doing the repetitiveness of a worksheet is important, but also doing the math games and word problems Piaget would suggest, are even more important. I want to de-emphasize having the correct answers in favor for helping my children learn how to derive answers on their own and be able to defend their results to me, answers they came up with after a struggle. If their answer is wrong, I want them to be able to work through it until they get it right.
As they get older, I like the idea of gaining an overall awareness and an ability to identify problems that have mathematical solutions. I would probably emphasize statistics and probability and linear algebra over calculus. I just think probability and statistics is so important, so relevant in day to day life. Calculus is also cool and important, but maybe not as much so unless a science or engineering degree is pursued (basic calculus should be known, though. Basic calculus is really not that hard and can be learned by most anyone).
We'll see, but my personal goal is that every single one of my kids both enjoy and be good at math.


Q.Find the LCM of 96 and 360 by using the fundamental theorem of arithmetic.
Q.What will be the least number that is divisible by all the numbers from 1 to 10 (both inclusive).
Q. What will be two natural numbers whose sum is 85 and the least common multiple is 102?
Ans. 51 & 34
Q.The HCF and LCM of two numbers are 33 and 264 respectively. When the first number is completely divided by 2 the quotient is 33. find the other number.
Q. Given that HCF (306, 657) = 9, find LCM (306, 657).
Q. Find the largest number which divides 70 and 125 leaving the remainder 5 and 8 respectively.
Q.The decimal expansion of π is terminating or nonterminating non-recurring.
Ans. nonterminating non-recurring
Q.HCF and LCM of two numbers are 9 and 459 respectively. If one of the numbers is 27, find the other number.
Q. Find the HCF (865, 255) using Euclid’s division lemma.
Q.Find HCF and LCM of 13 and 17 by prime factorization method.


KEY STAGE 3
Key Stage 3 students should have at least two x 30 minutes of homework for English, Maths and Science each week. For all other subjects, one x 30 minutes homework will be set. Subject teachers will inform students of the day this homework will be set and when it should be completed.
KEY STAGE 4
Key Stage 4 students will be set up to 1.5 hours homework for each GCSE each week. English Language and English Literature count as two GCSEs. Double Science counts as two GCSEs and Triple Science is worth three GCSEs. Subject teachers will inform students of the day this homework will be set and when it should be completed.
KEY STAGE 5
Key Stage 5 students will have at least two x 1 hour of homework per option subject each week. In addition to this, it is expected that, for each subject being studied, 5 hours individual study per week will be carried out by the student.


統計分析でさらなるサポートが必要な場合は、Editage’s Statistical Review Serviceをはじめとする専門の出版支援サービスの力を借りるのもよいでしょう。
Regression analysis is a statistical method used to predict the value of one variable based on the value of another variable. In regression analysis, one variable is considered the independent variable, while the other is the dependent variable. The goal of regression analysis is to find the best-fit line that describes the relationship between the independent variable and the dependent variable. This line can then be used to make predictions about the value of the dependent variable based on the value of the independent variable. Regression analysis also provides information about the strength and direction of the relationship between the two variables.
On the other hand, correlation analysis is a statistical method used to measure the degree of association between two variables. In correlation analysis, both variables are treated as being equal in importance, and the goal is to measure the strength and direction of their relationship. Correlation analysis provides a correlation coefficient that ranges from -1 to 1, where a value of -1 indicates a perfect negative correlation, a value of 0 indicates no correlation, and a value of 1 indicates a perfect positive correlation. Read more Ethical Hacking Course in Pune
In summary, regression analysis is used to predict the value of one variable based on the value of another variable, while correlation analysis is used to measure the strength and direction of the relationship between two variables.
Whether to use correlation analysis or regression analysis depends on the dataset and objectives of the study pay someone to do my online classes is used to quantitatively describe the relationship between two variables . Correlation analysis evaluates the linear relationship between two variables by calculating the correlation coefficient, which indicates how much the other variable changes when one variable changes .
Educational Technologies Comparison is a related technique of correlation analysis that evaluates the relationship between outcome variables and risk factors or confounding variables. Outcome variables are also called response or dependent variables, and risk factors and confounding variables are also called predictor or independent variables. Regression analysis is useful when you need to identify the effect of unit changes in a known variable (x)(y)
If you need additional help with your statistical analysis, you may want to seek help from a professional publication support service such as Editage's Statistical Review Service .


Expressways To Math®
Level 1 and A-Vip
Expressways To Math® makes use of the same A-VIP techniques used in Expressways To Reading®. A-VIP is used to embed into permanent memory the fundamental facts of calculation. Expressways To Math® then exercises and integrates the accumulating memory base so that the learner can apply it to developing math skills through computer games, recitation to music and rhythms, and contests.
Level I simultaneously integrates number theory, counting, addition, subtraction, multiplication and division as applied to practical situations. The goal is to commit to memory the 400 math facts, to understand counting and number theory, and to be able to recall the facts for quick mental applications. When the math facts are thoroughly mastered, students often generate computational speeds that rival electronic calculators.
Level 2 and Level 3
Expressways To Math® Level II involves telling time, handling money, inequalities, types of fractions and their processes, decimal numbers, ruler fractions, percentages, measurements in the English and metric systems, basic plane geometric forms, averages, estimates, ratio, probability, exponents, circles, areas of circles, volumes of solids, negative numbers, temperature and some advanced lessons for logical thinkers. Each lesson has an interactive instructional introduction before a student is directed to perform the exercises.
Level III consists of the section of word problems from QuikComp®.


In the Microblading method we use the Divine Caliper (Golden Section). It is a tool for measuring the phi proportion (Φ= 1.618), which is a proportion used by nature in the modelling of life. The Golden Section is an ancient symbol of perfection and beauty.
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The Divine Caliper tool for eyebrow shaping is used to determine the correct proportions of the eyebrows and face. By doing so, the face gains correct symmetry and balance, while the eyebrows gain their function.
“The golden ratio also is called the golden mean or golden section.”
The golden ratio also is called the golden mean or golden section. Other names include extreme and mean ratio, medial section, divine proportion, divine section, golden proportion, golden cut, and golden number.
Some twentieth-century artists and architects, including Le Corbusier and Dalí, have proportioned their works to approximate the golden ratio—especially in the form of the golden rectangle, in which the ratio of the longer side to the shorter is the golden ratio—believing this proportion to be aesthetically pleasing. The golden ratio appears in some patterns in nature, including the spiral arrangement of leaves and other plant parts.
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Math round 4
It has been an amazing round learning about percentages and angles. I love learning percentages since I was in grade 4 because in my perspective, it is easy to understand and a very handy way of writing fractions. Percentage can be compared more easily than fractions, and it is short and easy to remember because the number of percentages are mostly used from 1 to 100, so you don’t have to remember a lot of numbers. Percentages are also useful in the way that we use to look at the increasing and decreasing. For an example: if we want to look at how many people have died due to the traffic accident between 2016 and 2017, the only way that is easy for us to know is looking at the percentage. If it says “From 2016 and 2017, the number of people that died due to the traffic accident has increased by 10%”, so by looking at that percentage, we know that it does not decrease that much. Percentages are now over and heads up to Angles.
Learning angles is a little hard for me because I am not really good at it, but I told myself that “you are going to be an Angles master one day”. I know that I am not good at it, so I try to work on both the exercises about angles in the book and on Khan Academy. Doing exercises more and more help me a lot with Angles, so now I am pretty good at it. In geometry, an Angle is a place where two lines intersect and the until of an angle is degree. There are many types of angles that we name in mathematics and I will define them below.
- Right: is an angle that is exactly 90 degrees
- Straight: is an angle that is exactly 180 degrees
- Acute: is an angle that is less than 90 degrees
- Obtuse: is an angle that is greater than 90 degrees and less than 180 degrees
- Reflex: is an angle measuring greater than 180 degrees and less than 360 degrees
- Vertically opposite: is an angle opposite each other when two lines cross and they are always equal.
- Complementary: Two Angles are Complementary when they add up to 90 degrees
- Supplementary: is an angle that make a straight line and measures add up to 180 degrees.
- Angles around: is an angle that is always add up to 360 degree


In Year 3, we have just completed our topic on Superheroes. During the Superheroes topic, we created our own superheroes and decided what their special power would be.
Next week we are starting our brand new topic ’Around the World in 80 days.’ In this topic, we will be looking at world maps, countries and continents. We will be planning a route around the world on a map and deciding which continents and countries we would like to visit and why. As well as looking at world maps, we are also going to be focusing our learning around festivals and traditions around the world.
In maths this half term, we are focusing on place value (hundreds, tens and units) and the four operations (addition, subtraction, multiplication and subtraction). We are also learning our 3 and 4 times tables.


Information used to be analog, having many different values. Over time, the computer revolution has reduced the the number of different values to 1's and 0's. And yet, it is the author of those numbers to express infinity: The constant PI, which is used to calculate the circumference and volume of circles and spheres based on their diameter or radius is an infinite, non-repeating real number, which has a number of very intriguing consequences: because of its infinity, the number has every possible sequence of digits in it. Likewise, converted to ASCII or any other character set, they would also at some point contains the complete unabridged works of William Shakespeare, Robert A. Heinlein, or whatever author you would want to read. Heck, if we were to chop it up in image-sized bitmaps of a given type, we would find every image imaginable in there, and even all frames of every movie that has ever been made! Are you impressed with the size of infinite possibilities yet? All in the shape of the humble pie!


SAT computes a scaled scored based on your raw scores.
This is the number of points you earned, based on the number of questions you answered correctly, minus a fraction of the number answered incorrectly. Questions you skipped are not counted and no points are subtracted for incorrect Student Produced Response mathematics questions (grid-ins).
Your raw score is then converted to a scaled score (reported on a 200-800 scale) by a statistical process called equating to ensure that the different forms of the test or the level of ability of the students with whom you are tested do not affect your score.
You will get a scaled score (reported on a 200-800 scale) in this manner for your Critical Reading and Mathematics sections.
The scores for the writing section has two components and one composite scaled score.
Every SAT Reasoning test includes a 25-minute section, which doesn't count toward your final score. Please remember that you won't be able to tell which section is the unscored section. It may be a critical reading, mathematics, or multiple-choice writing section.


2 edition of Limit theorems for transient Markov chains found in the catalog.
Limit theorems for transient Markov chains
Sidney C. Port
Bibliography: p. 35.
|Statement||Sidney C. Port.|
|Series||Memorandum -- RM-4965-PR, Research memorandum (Rand Corporation) -- RM-4965-PR.|
|The Physical Object|
|Pagination||v, 35 p. ;|
|Number of Pages||35|
This paper studies aspects of the Siegmund dual of the Markov branching process. The principal results are optimal convergence rates of its transition function and limit theorems in the case that it is not positive recurrent. Additional discussion is given about specifications of the Markov branching process and its dual. The dualising Markov branching processes need not be regular or even Author: Anthony G. Pakes. On the Markov Chain Central Limit Theorem Galin L. Jones School of Statistics University of Minnesota Minneapolis, MN, USA [email protected] February 1, Abstract The goal of this expository paper is to describe conditions which guarantee a central limit theorem for functionals of general state space Markov chains. This is done with a view.
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An investigation of the asymptotic behavior of quantities in a countable state space transient Markov chain which include expressions of the times of the r-th visits to a finite, nonempty set of states. 1 Limiting distribution for a Markov chain In these Lecture Notes, we shall study the limiting behavior of Markov chains as time n!1.
In particular, under suitable easy-to-check conditions, we will see that a Markov chain possesses a limiting probability distribution, ˇ= (ˇ j) File Size: KB.
The state of such a system evolves according to some random dynamics without memory. The theory of Markov chains is the proper framework to study such random evolutions. In this chapter, Markov chains are introduced and their asymptotic behaviour is examined.
The abstract limit theorems will be applied later to various dynamic Monte Carlo : Gerhard Winkler. Book Description. Probability, Markov Chains, Queues, and Simulation provides a modern and authoritative treatment of the mathematical processes that underlie performance modeling.
The detailed explanations of mathematical derivations and numerous illustrative examples make this textbook readily accessible to graduate and advanced undergraduate students taking courses in which stochastic.
In this paper, we study a convergence theorem for a finite second-order Markov chain indexed by a general infinite tree with uniformly bounded degree.
Meanwhile, the strong law of large numbers (LLN) and Shannon-McMillan theorem for a finite second-order Markov chain indexed by this tree are obtained. Mathematics Subject Classification: 60F15; 60JCited by: 2.
Limit theorems for functionals of ergodic Markov chains with general state space. Memoirs of the American Mathematical Society, Mathematical Reviews (MathSciNet): MR Lecture Notes on Limit Theorems for Markov Chain Transition Probabilities (Mathematics Studies, No.
34) Paperback – January 1, by Steven Orey (Author) › Visit Amazon's Steven Orey Page. Find all the books, read about the author, and more. Cited by: The book looks like new. The content is very comprehensive and educational, good for beginners and advanced students and for researchers.
The simulation part is attractive. It's the kind of book that is worth having in your library for reference. Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance ModelingCited by: This book covers the classical theory of Markov chains on general state-spaces as well as many recent developments.
The theoretical results are illustrated by simple examples, many of which are taken from Markov Chain Monte Carlo methods. The book is self-contained, while all the results are carefully and concisely proven.
15 MARKOV CHAINS: LIMITING PROBABILITIES This is an irreducible chain, with invariant distribution π0 = π1 = π2 = 1 3 (as it is very easy to check). Moreover P2 = 0 0 1 1 0 0 0 1 0, P3 = I, P4 = P, etc. Although the chain does spend 1/3 of the time at each state, the transitionFile Size: 90KB.
The purpose of this paper is to obtain limit theorems for (S n (f)) n ≥ 0 when f ∈ L 2 (m) is centered and belongs to an appropriate subspace. The work of Kipnis and Varadhan  on the central limit theorem (CLT) for reversible Markov chains (P = P ⁎) inspired L.
Wu  and Olla  to approach the problem for non-symmetric P by Cited by: 4. Markov chains Section 1. What is a Markov chain. How to simulate one. Section 2. The Markov property. Section 3. How matrix multiplication gets into the picture. Section 4.
Statement of Limit theorems for transient Markov chains book Basic Limit Theorem about conver-gence to stationarity. A motivating example shows how compli-cated random objects can be generated using Markov Size: KB.
LIMIT THEOREMS FOR TRANSIENT MARKOV CHAINS Consequently, BR,(x, Y) -~ ~ nRo(X, z)Hn(z, Y), t~ 0 Z which establishes (). This completes the proof. The results of Theorem can be used to give interesting results on the time-dependent Dirichlet problem for a finite set B.
This problem. A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event.
In continuous-time, it is known as a Markov process. It is named after the Russian mathematician Andrey Markov. Markov chains have many applications as statistical models of real-world processes, such as studying cruise.
On the Markov Chain Central Limit Theorem Galin L. Jones School of Statistics University of Minnesota Minneapolis, MN, USA [email protected] Abstract The goal of this paper is to describe conditions which guarantee a central limit theorem for functionals of general state space Markov chains.
This is done with a view towards Markov. Limit theorems for one-dimensional transient random walks in Markov environments Article in Annales de l Institut Henri Poincaré Probabilités et Statistiques 40(5) September with. A System of Denumerably Many Transient Markov Chains. Several limit theorems are then proved for various functionals of this infinite particle system.
In particular, laws of large numbers and. The textbook looks at the fundamentals of probability theory, from the basic concepts of set-based probability, through probability distributions, to bounds, limit theorems, and the laws of large numbers.
Discrete and continuous-time Markov chains are analyzed from a. Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling - Ebook written by William J.
Stewart. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling/5(2). This paper studies Central Limit Theorems for real-valued functionals of Conditional Markov Chains.
Pre-vious work in this direction is a Central Limit Theorem by Xiang and Neville (), however, as this paper points out, the proof of their result is awed. Cen-tral Limit Theorems are File Size: KB.
6 CONTENTS B Mathematical tools B.1 Elementary conditional probabilities B.2 Some formulaes for sums and series B.3 Some results for matrices B.4 First order differential equations B.5 Second order linear recurrence equations B.6 The ratio test B.7 Integral test for convergence B.8 How to do certain computations in R C Proofs of selected results File Size: KB.
Thus, we can limit our attention to the case where our Markov chain consists of one recurrent class. In other words, we have an irreducible Markov chain. Note that as we showed in Examplein any finite Markov chain, there is at least one recurrent class.
Therefore, in finite irreducible chains, all states are recurrent. It turns out that. Keywords: central limit theorem, Markov chain, semigroups of linear operators. There are many proofs of the Central Limit Theorem for Markov chains which use linear oper- ators (Goldstein (), Johnson (, ), Kurtz (, ), Pinsky (), Trotter (, ).Cited by: 2.
Title: Limit Theorems for Transient Markov Chains Author: Sidney C. Port Subject: An investigation of the asymptotic behavior of quantities in a countable state space transient Markov chain which include expressions of the times of the r-th visits to a finite, nonempty set of states.
Recurrent and Transient States Deﬁnitions Relations between fi and p (n) ii Limiting Theorems for Generating Functions Applications to Markov Chains Relations Between fij and p (n) ij Periodic Processes Closed Sets Decomposition Theorem Remarks on Finite Chains Perron-Frobenius TheoremFile Size: KB.
Refinements of the central limit theorem for homogeneous Markov chains, in Limit Theorems of Probability Theory, N.M. Ostianu (ed.), Akad. Nauk SSSR, Moscow, – Nauk SSSR, Moscow, – Google ScholarAuthor: Anirban DasGupta. Central Limit Theorems for Conditional Markov Chains 2 CONDITIONAL MARKOV CHAINS Preliminaries.
Throughout this paper N, Z and R denote the sets of natural numbers, integers and real numbers, respectively. Consider the probability space (;F;P) with the following two stochastic processes de ned on it: X= (X t) t2Z are the observable variables. Irreducible Markov chains. If the state space is ﬁnite and all states communicate (that is, the Markov chain is irreducible) then in the long run, regardless of the initial condition, the Markov chain must settle into a steady state.
Formally, Theorem 3. An irreducible Markov chain Xn File Size: KB. Markov Chains. Welcome,you are looking at books for reading, the Markov Chains, you will able to read or download in Pdf or ePub books and notice some of author may have lock the live reading for some of ore it need a FREE signup process to obtain the book.
If it available for your country it will shown as book reader and user fully subscribe will benefit by having full access to. Probability, Markov Chains, Queues, and Simulation provides a modern and authoritative treatment of the mathematical processes that underlie performance modeling.
The detailed explanations of mathematical derivations and numerous illustrative examples make this textbook readily accessible to graduate and advanced undergraduate students taking courses in which stochastic processes play a.
Buy Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling by Stewart, William J. (ISBN: ) from Amazon's Book Store. Everyday low prices and free delivery on eligible orders/5(10).
Markov Chains and Applications Alexander olfoVvsky Aug Abstract In this paper I provide a quick overview of Stochastic processes and then quickly delve into a discussion of Markov Chains.
There is some as-sumed knowledge of basic calculus, probabilit,yand matrix theory. I build up Markov Chain theory towards a limit theorem. Probability, Markov Chains, Queues, and Simulation provides a modern and authoritative treatment of the mathematical processes that underlie performance modeling.
The detailed explanations of mathematical derivations and numerous illustrative examples make this textbook readily accessible to graduate and advanced undergraduate students taking courses in which stochastic processes play a Price: $ 4.
SOME LIMIT THEOREMS FOR POSITIVE RECURRENT BRANCHING MARKOV CHAINS 86 Some Preliminary Results 86 Discrete State Space Case 95 Notations, Definitions, and Assumptions 95 Law of Large Numbers 97 Large Deviation Continuous State Space Case Notations and Definitions Characteristics of Markov Chains.
Now that we’re comfortable with the basic theory behind Markov processes, we’ll talk about some common properties that we use to describe different Markov Chains. Concept Recurrent vs. Transient (state characteristic). We will limit ourselves to homogeneous Markov Chains. Or Markov Chains that do not evolve in time.
De nition We say that a Markov Chain is homogeneous if its one-step tran-sition probabilities do not depend on n ie. 8n;m2N and i;j2Z p ij(n) = p ij(m) We then de ne the n-step transition probabilities of a homogeneous Markov Chain by p(m) ij File Size: 97KB.
Limit Theorems for the Sample Entropy of Hidden Markov Chains Guangyue Han University of Hong Kong email: [email protected] Janu Abstract Recently, based on the Shannon-McMillan-Breiman theorem, eﬃcient Monte Carlo methods for approximating the entropy rate of a hidden Markov chain were proposed.
STABLE LIMIT LAWS FOR MARKOV CHAINS 3 Doeblin’s idea, can be found in an early paper of Nagaev , assuming a strong Doeblin condition.
Starting from the early sixties, another, more analytical approach, has been developed for proving central limit theorems for Markov chains, based on a martingale approximation of the additive Size: KB.
Difference between Recurrent state and Transient state in Markov Chain i) A state ‘i’ is called Recurrent, if we go from that state to any other state ‘j’, then there is at least one path to return back to ‘i’. On the other hand, there will be at. Markov Chains - 2 State Classification Accessibility • State j is accessible from state i if p ij (n) >0 for some n>= 0, meaning that starting at state i, there is a positive probability of transitioning to state j inFile Size: 2MB.
I read Bremaud's "Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues" before this book, which left me rather confused. Norris, on the other hand, is quite lucid, and helps the reader along with examples to build intuition in the beginning.
Disclaimer: I am a non-mathematician, and mostly try to learn those tools that apply to my area/5(2).The textbook looks at the fundamentals of probability theory, from the basic concepts of set-based probability, through probability distributions, to bounds, limit theorems, and the laws of large numbers.
Discrete and continuous-time Markov chains are analyzed from a 5/5(3).Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling July


We had a great conversation with Kevin Hurley, a recent graduate from Fullstack Academy. Kevin is a high school math teacher who caught the coding bug and decided to switch careers. He's exactly the kind of self-motivated technologist that will thrive our the tech economy. So, if you are thinking about jumping into the tech space, or you're a CTO looking to hire talent from "outside the box" -- then come have a listen. One more thing ... if Chevy Chase is reading this description, we assure him that in this episode there will be NO math.