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Author: Jordan M. Stoyanov
Publisher: Courier Corporation
Published: 2013-09-18
Total Pages: 404
ISBN-13: 0486315932
DOWNLOAD EBOOK →Most mathematical examples illustrate the truth of a statement; counterexamples demonstrate a statement's falsity. This third edition features the author's revisions and corrections plus a substantial new appendix. 2012 edition.
Author: A.F. Siegel
Publisher: Routledge
Published: 2017-11-22
Total Pages: 387
ISBN-13: 1351457632
DOWNLOAD EBOOK →This volume contains six early mathematical works, four papers on fiducial inference, five on transformations, and twenty-seven on a miscellany of topics in mathematical statistics. Several previously unpublished works are included.
Author: Gary L. Wise
Publisher: Oxford University Press
Published: 1993-10-07
Total Pages: 224
ISBN-13: 9780195361308
DOWNLOAD EBOOK →A counterexample is any example or result that is the opposite of one's intuition or to commonly held beliefs. Counterexamples can have great educational value in illuminating complex topics that are difficult to explain in a rigidly logical, written presentation. For example, ideas in mathematical sciences that might seem intuitively obvious may be proved incorrect with the use of a counterexample. This monograph concentrates on counterexamples for use at the intersection of probability and real analysis, which makes it unique among such treatments. The authors argue convincingly that probability theory cannot be separated from real analysis, and this book contains over 300 examples related to both the theory and application of mathematics. Many of the examples in this collection are new, and many old ones, previously buried in the literature, are now accessible for the first time. In contrast to several other collections, all of the examples in this book are completely self-contained--no details are passed off to obscure outside references. Students and theorists across fields as diverse as real analysis, probability, statistics, and engineering will want a copy of this book.
Author: A.F. Siegel
Publisher: Routledge
Published: 2017-11-22
Total Pages: 328
ISBN-13: 1351457640
DOWNLOAD EBOOK →This volume contains six early mathematical works, four papers on fiducial inference, five on transformations, and twenty-seven on a miscellany of topics in mathematical statistics. Several previously unpublished works are included.
Author: Jordan M. Stoyanov
Publisher: Wiley
Published: 1997-07-14
Total Pages: 0
ISBN-13: 9780471965381
DOWNLOAD EBOOK →Counterexamples (in the mathematical sense) are powerful tools of mathematical theory. This book covers counterexamples from probability theory and stochastic processes. This new expanded edition includes many examples and the latest research results. The author is regarded as one of the foremost experts in the field. Contains numbers examples.
Author: Lynn Arthur Steen
Publisher: Courier Corporation
Published: 2013-04-22
Total Pages: 274
ISBN-13: 0486319296
DOWNLOAD EBOOK →Over 140 examples, preceded by a succinct exposition of general topology and basic terminology. Each example treated as a whole. Numerous problems and exercises correlated with examples. 1978 edition. Bibliography.
Author: Bernard R. Gelbaum
Publisher: Courier Corporation
Published: 2012-07-12
Total Pages: 224
ISBN-13: 0486134911
DOWNLOAD EBOOK →These counterexamples deal mostly with the part of analysis known as "real variables." Covers the real number system, functions and limits, differentiation, Riemann integration, sequences, infinite series, functions of 2 variables, plane sets, more. 1962 edition.
Author: A. A. Sveshnikov
Publisher: Courier Corporation
Published: 2012-04-30
Total Pages: 512
ISBN-13: 0486137562
DOWNLOAD EBOOK →Approximately 1,000 problems — with answers and solutions included at the back of the book — illustrate such topics as random events, random variables, limit theorems, Markov processes, and much more.
Author: Bernard R. Gelbaum
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 339
ISBN-13: 1461209935
DOWNLOAD EBOOK →The gratifying response to Counterexamples in analysis (CEA) was followed, when the book went out of print, by expressions of dismay from those who were unable to acquire it. The connection of the present volume with CEA is clear, although the sights here are set higher. In the quarter-century since the appearance of CEA, mathematical education has taken some large steps reflected in both the undergraduate and graduate curricula. What was once taken as very new, remote, or arcane is now a well-established part of mathematical study and discourse. Consequently the approach here is designed to match the observed progress. The contents are intended to provide graduate and ad vanced undergraduate students as well as the general mathematical public with a modern treatment of some theorems and examples that constitute a rounding out and elaboration of the standard parts of algebra, analysis, geometry, logic, probability, set theory, and topology. The items included are presented in the spirit of a conversation among mathematicians who know the language but are interested in some of the ramifications of the subjects with which they routinely deal. Although such an approach might be construed as demanding, there is an extensive GLOSSARY jlNDEX where all but the most familiar notions are clearly defined and explained. The object ofthe body of the text is more to enhance what the reader already knows than to review definitions and notations that have become part of every mathematician's working context.
Author: Howard G. Tucker
Publisher: Academic Press
Published: 2014-06-27
Total Pages: 288
ISBN-13: 1483220508
DOWNLOAD EBOOK →Probability and Mathematical Statistics: A Series of Monographs and Textbooks: A Graduate Course in Probability presents some of the basic theorems of analytic probability theory in a cohesive manner. This book discusses the probability spaces and distributions, stochastic independence, basic limiting operations, and strong limit theorems for independent random variables. The central limit theorem, conditional expectation and martingale theory, and Brownian motion are also elaborated. The prerequisite for this text is knowledge of real analysis or measure theory, particularly the Lebesgue dominated convergence theorem, Fubini's theorem, Radon-Nikodym theorem, Egorov's theorem, monotone convergence theorem, and theorem on unique extension of a sigma-finite measure from an algebra to the sigma-algebra generated by it. This publication is suitable for a one-year graduate course in probability given in a mathematics program and preferably for students in their second year of graduate work.
