Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

October 29, 2012

Linear Algebra: As an Introduction to Abstract Mathematics

Filed under: Mathematics — Patrick Durusau @ 4:28 pm

Linear Algebra: As an Introduction to Abstract Mathematics by Isaiah Lankham, Bruno Nachtergaele and Anne Schilling.

From the cover page:

Lecture Notes for MAT67, University of California, Davis, written Fall 2007, last updated October 9, 2011.

Organized to teach both the computational as well as abstract (proof) side of linear algebra.

Organizing a topic maps textbook along the same lines, computing (merging) between subject proxies as well as abstract theories of identification, could be quite useful.

Comments?

I first saw this at Christophe Lalanne’s A bag of tweets / October 2012.

October 11, 2012

Think Bayes: Bayesian Statistics Made Simple

Filed under: Bayesian Data Analysis,Bayesian Models,Mathematics,Statistics — Patrick Durusau @ 3:24 pm

Think Bayes: Bayesian Statistics Made Simple by Allen B. Downey.

Think Bayes is an introduction to Bayesian statistics using computational methods. This version of the book is a rough draft. I am making this draft available for comments, but it comes with the warning that it is probably full of errors.

Allen has written free books on Python, statistics, complexity and now Bayesian statistics.

If you don’t know his books, good opportunity to give them a try.

September 28, 2012

Mathematics at Google

Filed under: Mathematics — Patrick Durusau @ 2:12 pm

Mathematics at Google by Javier Tordable.

High spots history of Google with an emphasis on the mathematics that came into play.

Highly motivation!

I first saw this at: Four short links: 28 September 2012 by Nat Torkington.

Nat remarks this should encourage high school and college students to do their homework.

True, but post-college folk should also maintain math literacy.

Not just having math skills but also recognizing the unspoken assumptions in mathematical techniques.

September 24, 2012

Relatively Prime

Filed under: Mathematics — Patrick Durusau @ 4:34 pm

Relatively Prime: Stories from the Mathematical Domain

Stories about mathematics that I think will catch your interest.

I first saw this at Four short links: 24 September 2012 by Nat Torkington

September 23, 2012

Analysis of Boolean Functions

Filed under: Boolean Functions,Mathematics — Patrick Durusau @ 2:39 pm

Analysis of Boolean Functions. Course by Ryan O’Donnell.

The course description:

Boolean functions, f : {0,1}n → {0,1}, are perhaps the most basic object of study in theoretical computer science. They also arise in several other areas of mathematics, including combinatorics (graph theory, extremal combinatorics, additive combinatorics), metric and Banach spaces, statistical physics, and mathematical social choice.

In this course we will study Boolean functions via their Fourier transform and other analytic methods. Highlights will include applications in property testing, social choice, learning theory, circuit complexity, pseudorandomness, constraint satisfaction problems, additive combinatorics, hypercontractivity, Gaussian geometry, random graph theory, and probabilistic invariance principles.

If you look at the slides from Lecture One, 2007, you will see all the things that “boolean function” means across several disciplines.

It should also give you an incentive to keep up with the videos of the 2012 version.

September 17, 2012

Probability and Statistics Cookbook

Filed under: Mathematics,Probability,Statistics — Patrick Durusau @ 6:16 pm

Probability and Statistics Cookbook by Matthias Vallentin.

From the webpage:

The cookbook contains a succinct representation of various topics in probability theory and statistics. It provides a comprehensive reference reduced to the mathematical essence, rather than aiming for elaborate explanations.

Very summary presentation so better as a quick reminder type resource.

I was particularly impressed by the univariate distribution relationships map on the last page.

In that regard, you may want to look at John D. Cook’s Diagram of distribution relationships
and the links therein.

August 28, 2012

23 Mathematical Challenges [DARPA – A Modest Challenge]

Filed under: Challenges,Mathematics,Mathematics Indexing — Patrick Durusau @ 10:50 am

23 Mathematical Challenges [DARPA]

From the webpage:

Discovering novel mathematics will enable the development of new tools to change the way the DoD approaches analysis, modeling and prediction, new materials and physical and biological sciences. The 23 Mathematical Challenges program involves individual researchers and small teams who are addressing one or more of the following 23 mathematical challenges, which if successfully met, could provide revolutionary new techniques to meet the long-term needs of the DoD:

  • Mathematical Challenge 1: The Mathematics of the Brain
  • Mathematical Challenge 2: The Dynamics of Networks
  • Mathematical Challenge 3: Capture and Harness Stochasticity in Nature
  • Mathematical Challenge 4: 21st Century Fluids
  • Mathematical Challenge 5: Biological Quantum Field Theory
  • Mathematical Challenge 6: Computational Duality
  • Mathematical Challenge 7: Occam’s Razor in Many Dimensions
  • Mathematical Challenge 8: Beyond Convex Optimization
  • Mathematical Challenge 9: What are the Physical Consequences of Perelman’s Proof of Thurston’s Geometrization Theorem?
  • Mathematical Challenge 10: Algorithmic Origami and Biology
  • Mathematical Challenge 11: Optimal Nanostructures
  • Mathematical Challenge 12: The Mathematics of Quantum Computing, Algorithms, and Entanglement
  • Mathematical Challenge 13: Creating a Game Theory that Scales
  • Mathematical Challenge 14: An Information Theory for Virus Evolution
  • Mathematical Challenge 15: The Geometry of Genome Space
  • Mathematical Challenge 16: What are the Symmetries and Action Principles for Biology?
  • Mathematical Challenge 17: Geometric Langlands and Quantum Physics
  • Mathematical Challenge 18: Arithmetic Langlands, Topology and Geometry
  • Mathematical Challenge 19: Settle the Riemann Hypothesis
  • Mathematical Challenge 20: Computation at Scale
  • Mathematical Challenge 21: Settle the Hodge Conjecture
  • Mathematical Challenge 22: Settle the Smooth Poincare Conjecture in Dimension 4
  • Mathematical Challenge 23: What are the Fundamental Laws of Biology?

(Details of each challenge omitted. See the webpage for descriptions.)

Worthy mathematical challenges all but what about a more modest challenge? One that may help solve a larger one?

Such as cutting across the terminology barriers of approaches and fields of mathematics to collate the prior, present and ongoing research on each of these challenges?

Not only would the curated artifact be useful to researchers, but the act of curation, the reading and mapping of what is known on a particular problem, could spark new approaches to the main problem as well.

DARPA should consider a history curation project on one or more of these challenges.

Could produce a useful information artifact for researchers, train math graduate students in searching across approaches/fields, and might trigger a creative insight into a possible challenge solution.

I first saw this at Beyond Search: DARPA May Be Hilbert

August 27, 2012

Grinstead and Snell’s Introduction to Probability

Filed under: Mathematics,Probability — Patrick Durusau @ 2:37 pm

Grinstead and Snell’s Introduction to Probability

From the preface:

Probability theory began in seventeenth century France when the two great French mathematicians, Blaise Pascal and Pierre de Fermat, corresponded over two problems from games of chance. Problems like those Pascal and Fermat solved continued to influence such early researchers as Huygens, Bernoulli, and DeMoivre in establishing a mathematical theory of probability. Today, probability theory is a well-established branch of mathematics that finds applications in every area of scholarly activity from music to physics, and in daily experience from weather prediction to predicting the risks of new medical treatments.

This text is designed for an introductory probability course taken by sophomores, juniors, and seniors in mathematics, the physical and social sciences, engineering, and computer science. It presents a thorough treatment of probability ideas and techniques necessary for a firm understanding of the subject. The text can be used in a variety of course lengths, levels, and areas of emphasis.

What promises to be an entertaining and even literate book on probability.

I first saw this at Christopher Lalanne’s A bag of tweets / August 2012.

July 14, 2012

Text Mining Methods Applied to Mathematical Texts

Filed under: Indexing,Mathematics,Mathematics Indexing,Search Algorithms,Searching — Patrick Durusau @ 10:49 am

Text Mining Methods Applied to Mathematical Texts (slides) by Yannis Haralambous, Département Informatique, Télécom Bretagne.

Abstract:

Up to now, flexiform mathematical text has mainly been processed with the intention of formalizing mathematical knowledge so that proof engines can be applied to it. This approach can be compared with the symbolic approach to natural language processing, where methods of logic and knowledge representation are used to analyze linguistic phenomena. In the last two decades, a new approach to natural language processing has emerged, based on statistical methods and, in particular, data mining. This method, called text mining, aims to process large text corpora, in order to detect tendencies, to extract information, to classify documents, etc. In this talk I will present math mining, namely the potential applications of text mining to mathematical texts. After reviewing some existing works heading in that direction, I will formulate and describe several roadmap suggestions for the use and applications of statistical methods to mathematical text processing: (1) using terms instead of words as the basic unit of text processing, (2) using topics instead of subjects (“topics” in the sense of “topic models” in natural language processing, and “subjects” in the sense of various mathematical subject classifications), (3) using and correlating various graphs extracted from mathematical corpora, (4) use paraphrastic redundancy, etc. The purpose of this talk is to give a glimpse on potential applications of the math mining approach on large mathematical corpora, such as arXiv.org.

An invited presentation at CICM 2012.

I know Yannis from a completely different context and may comment on that in another post.

No paper but 50+ slides showing existing text mining tools can deliver useful search results, while waiting for a unified and correct index to all of mathematics. 😉

Varying semantics, as in all human enterprises, is an opportunity for topic map based assistance.

Conferences on Intelligent Computer Mathematics (CICM 2012)

Filed under: Conferences,Geometry,Knowledge Management,Mathematics,Mathematics Indexing — Patrick Durusau @ 10:34 am

Conferences on Intelligent Computer Mathematics (CICM 2012) (talks listing)

From the “general information” page:

As computers and communications technology advance, greater opportunities arise for intelligent mathematical computation. While computer algebra, automated deduction, mathematical publishing and novel user interfaces individually have long and successful histories, we are now seeing increasing opportunities for synergy among these areas.

The conference is organized by Serge Autexier (DFKI) and Michael Kohlhase (JUB), takes place at Jacobs University in Bremen and consists of five tracks

The overall programme is organized by the General Program Chair Johan Jeuring.

Which I located by following the conference reference in: An XML-Format for Conjectures in Geometry (Work-in-Progress)

A real treasure trove of research on searching, semantics, integration, focused on computers and mathematics.

Expect to see citations to work reported here and in other CICM proceedings.

An XML-Format for Conjectures in Geometry (Work-in-Progress)

Filed under: Geometry,Indexing,Keywords,Mathematical Reasoning,Mathematics,Ontology — Patrick Durusau @ 10:33 am

An XML-Format for Conjectures in Geometry (Work-in-Progress) by Pedro Quaresma.

Abstract:

With a large number of software tools dedicated to the visualisation and/or demonstration of properties of geometric constructions and also with the emerging of repositories of geometric constructions, there is a strong need of linking them, and making them and their corpora, widely usable. A common setting for interoperable interactive geometry was already proposed, the i2g format, but, in this format, the conjectures and proofs counterparts are missing. A common format capable of linking all the tools in the field of geometry is missing. In this paper an extension of the i2g format is proposed, this extension is capable of describing not only the geometric constructions but also the geometric conjectures. The integration of this format into the Web-based GeoThms, TGTP and Web Geometry Laboratory systems is also discussed.

The author notes open questions as:

  • The xml format must be complemented with an extensive set of converters allowing the exchange of information between as many geometric tools as possible.
  • The databases queries, as in TGTP, raise the question of selecting appropriate keywords. A fine grain index and/or an appropriate geometry ontology should be addressed.
  • The i2gatp format does not address proofs. Should we try to create such a format? The GATPs produce proofs in quite different formats, maybe the construction of such unifying format it is not possible and/or desirable in this area.

The “keywords,” “fine grained index,” “geometry ontology,” question yells “topic map” to me.

You?

PS: Converters and different formats also say “topic map,” just not as loudly to me. Your volume may vary. (YVMV)

.

July 5, 2012

Connect the Stars (Graphs Anyone?)

Filed under: Graphs,Mathematics,Networks — Patrick Durusau @ 7:59 am

Connect the Stars (How papers are like constellations ) by KW Regan.

From the post:

Bob Vaughan is a mathematician at Penn State University. He is also a Fellow of the Royal Society—not ours, Ben Franklin helped make it tough for us to have one about 236 years ago this Wednesday. He is a great expert on analytic number theory, especially applied to the prime numbers. His work involves the deep connections between integers and complex numbers that were first charted by Leonhard Euler in the time of Franklin.

Today we examine how connections are made in the literature, and how choosing them influences our later memory of what is known and what is not.

Proved mathematical statements are like stars of various magnitudes: claim, proposition, lemma, theorem… A paper usually connects several of the former kinds to a few bright theorems. Often there are different ways the connections could go, and a lengthened paper may extend them to various corollaries and other theorems. Thus we can get various constellations even from the same stars. Consider the Big Dipper and the larger Ursa Major:

I lack the mathematical chops to follow the substance of the post but can read along to see the connections that were made at different times by different people that contributed to what is reported as the present state of knowledge.

How to capture that, dare I say network/graph of interconnections?

Search seems haphazard and lossy.

Writing it out in prose monographs or articles isn’t much better because you still have to find the monograph or article.

What if there were a dynamic network/graph of connections that is overlaid and grows with publications? Both in the way of citations but less formal connections and to less than an entire article?

The social life of research as it is read, assimilated, used, revised and extended by members of a discipline.

That is to say that research isn’t separate from us, research is us. It is as much a social phenomena as prose, plays or poetry. Just written in a different style.

June 28, 2012

Heavy use of equations impedes communication among biologists

Filed under: Communication,Mathematics — Patrick Durusau @ 6:30 pm

Heavy use of equations impedes communication among biologists by Tim W. Fawcett and Andrew D. Higginson. (Proceedings of the National Academy of Sciences, June 25, 2012 DOI: 10.1073/pnas.1205259109)

Abstract:

Most research in biology is empirical, yet empirical studies rely fundamentally on theoretical work for generating testable predictions and interpreting observations. Despite this interdependence, many empirical studies build largely on other empirical studies with little direct reference to relevant theory, suggesting a failure of communication that may hinder scientific progress. To investigate the extent of this problem, we analyzed how the use of mathematical equations affects the scientific impact of studies in ecology and evolution. The density of equations in an article has a significant negative impact on citation rates, with papers receiving 28% fewer citations overall for each additional equation per page in the main text. Long, equation-dense papers tend to be more frequently cited by other theoretical papers, but this increase is outweighed by a sharp drop in citations from nontheoretical papers (35% fewer citations for each additional equation per page in the main text). In contrast, equations presented in an accompanying appendix do not lessen a paper’s impact. Our analysis suggests possible strategies for enhancing the presentation of mathematical models to facilitate progress in disciplines that rely on the tight integration of theoretical and empirical work.

I first saw this in Scientists Struggle With Mathematical Details, Study by Biologists Finds, where Higginson remarks on one intermediate solution:

Scientists need to think more carefully about how they present the mathematical details of their work. The ideal solution is not to hide the maths away, but to add more explanatory text to take the reader carefully through the assumptions and implications of the theory.

An excellent suggestion, considering that scientists don’t speak to each other in notation but in less precise natural language.

June 26, 2012

Journal of Statistical Software

Filed under: Mathematica,Mathematics,R,Statistics — Patrick Durusau @ 12:53 pm

Journal of Statistical Software

From the homepage:

Established in 1996, the Journal of Statistical Software publishes articles, book reviews, code snippets, and software reviews on the subject of statistical software and algorithms. The contents are freely available on-line. For both articles and code snippets the source code is published along with the paper.

Statistical software is the key link between statistical methods and their application in practice. Software that makes this link is the province of the journal, and may be realized as, for instance, tools for large scale computing, database technology, desktop computing, distributed systems, the World Wide Web, reproducible research, archiving and documentation, and embedded systems.

We attempt to present research that demonstrates the joint evolution of computational and statistical methods and techniques. Implementations can use languages such as C, C++, S, Fortran, Java, PHP, Python and Ruby or environments such as Mathematica, MATLAB, R, S-PLUS, SAS, Stata, and XLISP-STAT.

There are currently 518 articles, 34 code snippets, 104 book reviews, 6 software reviews, and 13 special volumes in our archives. These can be browsed or searched. You can also subscribe for notification of new articles.

Running down resources used in Wordcloud of the Arizona et al. v. United States opinion when I encountered this wonderful site.

I have only skimmed the surface for an article or two in particular so can’t begin to describe the breadth of material you will find here.

I am sure I will be returning time and time again to this site. Suggest if you are interested in statistical manipulation of data that you do the same.

June 17, 2012

How to Read Mathematics

Filed under: Mathematics — Patrick Durusau @ 3:30 pm

How to Read Mathematics by Shai Simonson and Fernando Gouvea.

From the post:

A reading protocol is a set of strategies that a reader must use in order to benefit fully from reading the text. Poetry calls for a different set of strategies than fiction, and fiction a different set than non-fiction. It would be ridiculous to read fiction and ask oneself what is the author’s source for the assertion that the hero is blond and tanned; it would be wrong to read non-fiction and not ask such a question. This reading protocol extends to a viewing or listening protocol in art and music. Indeed, much of the introductory course material in literature, music and art is spent teaching these protocols.

Mathematics has a reading protocol all its own, and just as we learn to read literature, we should learn to read mathematics. Students need to learn how to read mathematics, in the same way they learn how to read a novel or a poem, listen to music, or view a painting. Ed Rothstein’s book, Emblems of Mind, a fascinating book emphasizing the relationship between mathematics and music, touches implicitly on the reading protocols for mathematics.

When we read a novel we become absorbed in the plot and characters. We try to follow the various plot lines and how each affects the development of the characters. We make sure that the characters become real people to us, both those we admire and those we despise. We do not stop at every word, but imagine the words as brushstrokes in a painting. Even if we are not familiar with a particular word, we can still see the whole picture. We rarely stop to think about individual phrases and sentences. Instead, we let the novel sweep us along with its flow and carry us swiftly to the end. The experience is rewarding, relaxing and thought provoking.

Novelists frequently describe characters by involving them in well-chosen anecdotes, rather than by describing them by well-chosen adjectives. They portray one aspect, then another, then the first again in a new light and so on, as the whole picture grows and comes more and more into focus. This is the way to communicate complex thoughts that defy precise definition.

Mathematical ideas are by nature precise and well defined, so that a precise description is possible in a very short space. Both a mathematics article and a novel are telling a story and developing complex ideas, but a math article does the job with a tiny fraction of the words and symbols of those used in a novel. The beauty in a novel is in the aesthetic way it uses language to evoke emotions and present themes which defy precise definition. The beauty in a mathematics article is in the elegant efficient way it concisely describes precise ideas of great complexity.

What are the common mistakes people make in trying to read mathematics? How can these mistakes be corrected?

Interesting post, along with a plug for the author’s new book, Rediscovering Mathematics. At a list price of $55 for a ~ 200 page hardback, I will be waiting for the paperback version.

Data mining is becoming more deeply intertwined with computer science and mathematics.

You can’t develop a facility for reading mathematics too soon. This is a good place to start.

(I first saw this at: Math Comprehension Made Easy.)

May 31, 2012

Mathematical Reasoning Group

Filed under: Logic,Mathematical Reasoning,Mathematics — Patrick Durusau @ 1:15 pm

Mathematical Reasoning Group

From the homepage:

The Mathematical Reasoning Group is a distributed research group based in the Centre for Intelligent Systems and their Applications, a research institute within the School of Informatics at the University of Edinburgh. We are a community of informaticists with interests in theorem proving, program synthesis and artificial intelligence. There is a more detailed overview of the MRG and a list of people. You can also find out how to join the MRG.

I was chasing down proceedings from prior “Large Heterogeneous Data” workshops (damn, that’s a fourth name), when I ran across this jewel as the location of some of the archives.

Has lots of other interesting papers, software, activities.

Sing out if you see something you think needs to appear on this blog.

April 26, 2012

Math is not “out there”

Filed under: Graphs,Mathematics — Patrick Durusau @ 6:29 pm

Number Line Is Learned, Not Innate Human Intuition

From the post:

Tape measures. Rulers. Graphs. The gas gauge in your car, and the icon on your favorite digital device showing battery power. The number line and its cousins — notations that map numbers onto space and often represent magnitude — are everywhere. Most adults in industrialized societies are so fluent at using the concept, we hardly think about it. We don’t stop to wonder: Is it “natural”? Is it cultural?

Now, challenging a mainstream scholarly position that the number-line concept is innate, a study suggests it is learned.

The study, published in PLoS ONE April 25, is based on experiments with an indigenous group in Papua New Guinea. It was led by Rafael Nunez, director of the Embodied Cognition Lab and associate professor of cognitive science in the UC San Diego Division of Social Sciences.

“Influential scholars have advanced the thesis that many of the building blocks of mathematics are ‘hard-wired’ in the human mind through millions of years of evolution. And a number of different sources of evidence do suggest that humans naturally associate numbers with space,” said Nunez, coauthor of “Where Mathematics Comes From” and co-director of the newly established Fields Cognitive Science Network at the Fields Institute for Research in Mathematical Sciences.

“Our study shows, for the first time, that the number-line concept is not a ‘universal intuition’ but a particular cultural tool that requires training and education to master,” Nunez said. “Also, we document that precise number concepts can exist independently of linear or other metric-driven spatial representations.”

I am not sure how “universal intuition[s]” regained currency but I am glad someone is sorting this out, again.

Universal intuition is a perennial mistake that attempts to put some “facts” beyond dispute. They are “universal.”

I concede the possibility that “universal” intuitions exist.

But advocates always have some particular “universal” intuition they claim to exist, which oddly enough supports some model or agenda of theirs.

Anecdotal evidence to be sure but I have never seen an advocate of a particular “universal” intuition pushing for one that was contrary to their model or agenda. Could just be coincidence but I leave that to your judgement.

I offer this study as a evidence you can cite in the face of “universal” intuitions in databases, ontologies, logic, etc. They are all cultural artifacts that we can use or leave as suits our then present purposes.

For more information see: Núñez R, Cooperrider K, Wassmann J. Number Concepts without Number Lines in an Indigenous Group of Papua New Guinea. PLoS ONE, 7(4): e35662 DOI: 10.1371/journal.pone.0035662

March 29, 2012

Introduction to Real Analysis

Filed under: Mathematics — Patrick Durusau @ 6:39 pm

Introduction to Real Analysis by William F. Trench.

From the introduction:

This is a text for a two-term course in introductory real analysis for junior or senior mathematics majors and science students with a serious interest in mathematics. Prospective educators or mathematically gifted high school students can also benefit from the mathematical maturity that can be gained from an introductory real analysis course.

The book is designed to fill the gaps left in the development of calculus as it is usually presented in an elementary course, and to provide the background required for insight into more advanced courses in pure and applied mathematics. The standard elementary calculus sequence is the only specific prerequisite for Chapters 1–5, which deal with real-valued functions. (However, other analysis oriented courses, such as elementary differential equation, also provide useful preparatory experience.) Chapters 6 and 7 require a working knowledge of determinants, matrices and linear transformations, typically available from a first course in linear algebra. Chapter 8 is accessible after completion of Chapters 1–5.

Without taking a position for or against the current reforms in mathematics teaching, I think it is fair to say that the transition from elementary courses such as calculus, linear algebra, and differential equations to a rigorous real analysis course is a bigger step today than it was just a few years ago. To make this step today’s students need more help than their predecessors did, and must be coached and encouraged more. Therefore, while striving throughout to maintain a high level of rigor, I have tried to write as clearly and informally as possible. In this connection I find it useful to address the student in the second person. I have included 295 completely worked out examples to illustrate and clarify all major theorems and definitions.

I have emphasized careful statements of definitions and theorems and have tried to be complete and detailed in proofs, except for omissions left to exercises. I give a thorough treatment of real-valued functions before considering vector-valued functions. In making the transition from one to several variables and fromreal-valued to vector-valued functions, I have left to the student some proofs that are essentially repetitions of earlier theorems. I believe that working through the details of straightforward generalizations of more elementary results is good practice for the student.

Great care has gone into the preparation of the 760 numbered exercises, many with multiple parts. They range from routine to very difficult. Hints are provided for the more difficult parts of the exercises.

Between this and the Mathematics for Computer Science book, you should not have to buy anything for your electronic book reader this summer. 😉

I saw this mentioned on Christophe Lalanne’s Bag of Tweets for March 2012.

Mathematics for Computer Science

Filed under: CS Lectures,Mathematics — Patrick Durusau @ 6:39 pm

Mathematics for Computer Science, by Eric Lehman, F Thomson Leighton, and Albert R Meyer.

Videos, slides, class problems, miniquizes, and reading material, including the book by the same name. There are officially released parts of the book and a draft of the entire work. Has a nice section on graphs.

I saw the book mentioned in Christophe Lalanne’s Bag of Tweets for March 2012 and then back tracked to the class site.

March 26, 2012

The Difference Between Interaction and Association

Filed under: Mathematics,Statistics — Patrick Durusau @ 6:35 pm

The Difference Between Interaction and Association by Karen Grace-Martin.

From the post:

It’s really easy to mix up the concepts of association (a.k.a. correlation) and interaction. Or to assume if two variables interact, they must be associated. But it’s not actually true.

In statistics, they have different implications for the relationships among your variables, especially when the variables you’re talking about are predictors in a regression or ANOVA model.

Association

Association between two variables means the values of one variable relate in some way to the values of the other. Association is usually measured by correlation for two continuous variables and by cross tabulation and a Chi-square test for two categorical variables.

Unfortunately, there is no nice, descriptive measure for association between one categorical and one continuous variable, but either one-way analysis of variance or logistic regression can test an association (depending upon whether you think of the categorical variable as the independent or the dependent variable).

Essentially, association means the values of one variable generally co-occur with certain values of the other.

Interaction

Interaction is different. Whether two variables are associated says nothing about whether they interact in their effect on a third variable. Likewise, if two variables interact, they may or may not be associated.

An interaction between two variables means the effect of one of those variables on a third variable is not constant—the effect differs at different values of the other.

You will most likely be using statistics or at least discussing topic maps with analysts who use statistics so be prepared to distinguish “association” in the statistics sense from association when you use it in the topic maps sense. They are pronounced the same way. 😉

Depending upon the subject matter of your topic map, you may well be describing “interaction,” but again, not in the sense that Karen illustrates in her post.

The world of semantics is a big place so be careful out there.

March 7, 2012

Mathics

Filed under: Mathematics,Mathics — Patrick Durusau @ 5:42 pm

Mathics

From the website:

Mathics is a free, general-purpose online computer algebra system featuring Mathematica-compatible syntax and functions. It is backed by highly extensible Python code, relying on SymPy for most mathematical tasks and, optionally, Sage for more advanced stuff.

A general mathematics package that self-describes some of its needs as folows:

Apart from performance issues, new features like 3D graphics and more functions in various mathematical fields like calculus, number theory, or graph theory are still to be added. (http://www.mathics.net/doc/manual/introduction/what-is-missing/)

As you explore graphs and other structures, you might want to consider contributing to this project.

January 18, 2012

Statistics 110: Introduction to Probability

Filed under: Mathematics,Statistics — Patrick Durusau @ 7:52 pm

Statistics 110: Introduction to Probability by Joseph Blitzstein.

Description:

Statistics 110 (Introduction to Probability), taught at Harvard University by Joe Blitzstein in Fall 2011. Lecture videos, homework, review material, practice exams, and a large collection of practice problems with detailed solutions are provided. This course is an introduction to probability as a language and set of tools for understanding statistics, science, risk, and randomness. The ideas and methods are useful in statistics, science, philosophy, engineering, economics, finance, and everyday life. Topics include the following. Basics: sample spaces and events, conditional probability, Bayes’ Theorem. Random variables and their distributions: cumulative distribution functions, moment generating functions, expectation, variance, covariance, correlation, conditional expectation. Univariate distributions: Normal, t, Binomial, Negative Binomial, Poisson, Beta, Gamma. Multivariate distributions: joint, conditional, and marginal distributions, independence, transformations, Multinomial, Multivariate Normal. Limit theorems: law of large numbers, central limit theorem. Markov chains: transition probabilities, stationary distributions, reversibility, convergence.

Like Michael Heise, I haven’t watched the lectures but I would appreciate hearing comments from anyone who does.

Particularly in an election year where people are going to be using (mostly abusing) statistics to influence your vote in city, county (parish in Louisiana), state and federal elections.

First seen at Statistics via iTunes by Michael Heise.

January 12, 2012

Probably Overthinking It

Filed under: Mathematics,Statistics — Patrick Durusau @ 7:28 pm

Probably Overthinking It: A blog by Allen Downey about statistics and probability.

If your work has any aspect of statistics/probability about it, you probably need to be reading this blog.

I commend it to topic mappers because claims about data are often expressed as statistics.

Not to mention that the results of statistics are subjects themselves, which you may wish to include in your topic map.

December 31, 2011

FreeBookCentre.Net

Filed under: Books,Computer Science,Mathematics — Patrick Durusau @ 7:20 pm

FreeBookCentre.Net

Books and online materials on:

  • Computer Science
  • Physics
  • Mathematics
  • Electronics

I just scanned a few of the categories and the coverage isn’t systematic. Still, if you need a text for quick study, the price is right.

December 28, 2011

400 Free Online Courses from Top Universities

Filed under: CS Lectures,Mathematics,Statistics — Patrick Durusau @ 9:37 pm

400 Free Online Courses from Top Universities

Just in case hard core math/cs stuff isn’t your cup of tea or you want to write topic maps about some other area of study, this may be a resource for you.

Oddly enough (?), every listing of free courses seems to be different from other listings of free courses.

If you happen to run across seminar lectures (graduate school) on Ancient or Medieval philosophy, drop me a line. Or even better, on individual figures.

I first saw this linked on John Johnson’s Realizations in Biostatistics. John was pointing to the statistics/math courses but there is a wealth of other material as well.

Visualizing 4+ Dimensions

Filed under: Dimension Reduction,Dimensions,Mathematics — Patrick Durusau @ 9:36 pm

Visualizing 4+ Dimensions

From the post:

When people realize that I study pure math, they often ask about how to visualize four or more dimensions. I guess it’s a natural question to ask, since mathematicians often have to deal with very high (and sometimes infinite) dimensional objects. Yet people in pure math never really have this problem.

Pure mathematicians might like you to think that they’re just that much smarter. But frankly, I’ve never had to visualize anything high-dimensional in my pure math classes. Working things out algebraically is much nicer, and using a lower-dimensional object as an example or source of intuition usually works out — at least at the undergrad level.

But that’s not a really satisfying answer, for two reasons. One is that it is possible to visualize high-dimensional objects, and people have developed many ways of doing so. Dimension Math has on its website a neat series of videos for visualizing high-dimensional geometric objects using stereographic projection. The other reason is that while pure mathematicians do not have a need for visualizing high-dimensions, statisticians do. Methods of visualizing high dimensional data can give useful insights when analyzing data.

This is an important area for study, but not only because identifications can consist of values in multiple dimensions.

It is important because the recognition of an identifier can also consist of values spread across multiple dimensions.

More on that second statement before year’s end (so you don’t have to wait very long, just until holiday company leaves).

I first saw this in Christophe Lalanne’s A bag of tweets / Dec 2011.

UCCS Department of Mathematics Math Courses

Filed under: Mathematics — Patrick Durusau @ 9:34 pm

UCCS Department of Mathematics Math Courses

I am sure everyone is wondering what math skills they can pick up this Spring. 😉 You will be glad to learn that University of Colorado at Colorado Springs (UCCS) has four online courses this Spring and an archive of more that fifty (50) more.

The classes are free but you do need to create an account to view the recorded content.

For the Spring 2012 Semester you will find:

  • Math 1360- Calculus II- Shannon Michaux- (MathOnline Course)
  • Math 2350- Calculus III – Dr. Jenny Dorrington – (MathOnline Course)
  • Math 3110- Theory of Numbers – Dr. Gene Abrams – (MathOnline Course)
  • Math 3400- Introduction to Differential Equations – Dr. Radu Cascaval – (MathOnline Course)

Videos are recorded and posted the same day as the class sessions. (No credit or certificates. But for some positions being able to do the job counts for a good bit.)

December 27, 2011

A Month of Math Software

Filed under: Mathematics — Patrick Durusau @ 7:15 pm

A Month of Math Software

For the month of November 2011 but past issues are also available. Really too much to quote or describe so go take a look and suggest anything you think needs to be mentioned specifically here.

Computer Vision & Math

Filed under: Image Recognition,Image Understanding,Mathematics — Patrick Durusau @ 7:10 pm

Computer Vision & Math

From the website:

The main part of this site is called Home of Math. It’s an online mathematics textbook that contains over 800 articles with over 2000 illustrations. The level varies from beginner to advanced.

Try our image analysis software. Pixcavator is a light-weight program intended for scientists and engineers who want to automate their image analysis tasks but lack a significant computing background. This image analysis software allows the analyst to concentrate on the science and lets us take care of the math.

If you create image analysis applications, consider Pixcavator SDK. It provides a simple tool for developing new image analysis software in a variety of fields. It allows the software developer to concentrate on the user’s needs instead of development of custom algorithms.

December 18, 2011

254B, Notes 1: Basic theory of expander graphs

Filed under: Graphs,Mathematics — Patrick Durusau @ 8:39 pm

254B, Notes 1: Basic theory of expander graphs

Tough sledding for non-mathematicians but cf. the citation to the Wikipedia article in the first sentence for the importance of this area for advanced topic maps.

The objective of this course is to present a number of recent constructions of expander graphs, which are a type of sparse but “pseudorandom” graph of importance in computer science, the theory of random walks, geometric group theory, and in number theory. The subject of expander graphs and their applications is an immense one, and we will not possibly be able to cover it in full in this course. In particular, we will say almost nothing about the important applications of expander graphs to computer science, for instance in constructing good pseudorandom number generators, derandomising a probabilistic algorithm, constructing error correcting codes, or in building probabilistically checkable proofs. For such topics, I recommend the survey of Hoory-Linial-Wigderson. We will also only pass very lightly over the other applications of expander graphs, though if time permits I may discuss at the end of the course the application of expander graphs in finite groups such as {SL_2(F_p)} to certain sieving problems in analytic number theory, following the work of Bourgain, Gamburd, and Sarnak.

Instead of focusing on applications, this course will concern itself much more with the task of constructing expander graphs. This is a surprisingly non-trivial problem. On one hand, an easy application of the probabilistic method shows that a randomly chosen (large, regular, bounded-degree) graph will be an expander graph with very high probability, so expander graphs are extremely abundant. On the other hand, in many applications, one wants an expander graph that is more deterministic in nature (requiring either no or very few random choices to build), and of a more specialised form. For the applications to number theory or geometric group theory, it is of particular interest to determine the expansion properties of a very symmetric type of graph, namely a Cayley graph; we will also occasionally work with the more general concept of a Schreier graph. It turns out that such questions are related to deep properties of various groups {G} of Lie type (such as {SL_2({\bf R})} or {SL_2({\bf Z})}), such as Kazhdan’s property (T), the first nontrivial eigenvalue of a Laplacian on a symmetric space {G/\Gamma} associated to {G}, the quasirandomness of {G} (as measured by the size of irreducible representations), and the product theory of subsets of {G}. These properties are of intrinsic interest to many other fields of mathematics (e.g. ergodic theory, operator algebras, additive combinatorics, representation theory, finite group theory, number theory, etc.), and it is quite remarkable that a single problem – namely the construction of expander graphs – is so deeply connected with such a rich and diverse array of mathematical topics. (Perhaps this is because so many of these fields are all grappling with aspects of a single general problem in mathematics, namely when to determine whether a given mathematical object or process of interest “behaves pseudorandomly” or not, and how this is connected with the symmetry group of that object or process.)

(There are also other important constructions of expander graphs that are not related to Cayley or Schreier graphs, such as those graphs constructed by the zigzag product construction, but we will not discuss those types of graphs in this course, again referring the reader to the survey of Hoory, Linial, and Wigderson.)

Follow with:

254B, Notes 2: Cayley graphs and Kazhdan’s property (T)

254B, Notes 3: Quasirandom groups, expansion, and Selberg’s 3/16 theorem

« Newer PostsOlder Posts »

Powered by WordPress