A Second Course in Probability
The 2006 INFORMS Expository Writing Award-winning and best-selling author Sheldon Ross (University of Southern California) teams up with Erol Peköz (Boston University) to bring you this textbook for undergraduate and graduate students in statistics, mathematics, engineering, finance, and actuarial science. This is a guided tour designed to give familiarity with advanced topics in probability without having to wade through the exhaustive coverage of the classic advanced probability theory books. Topics include measure theory, limit theorems, bounding probabilities and expectations, coupling and Stein's method, martingales, Markov chains, renewal theory, and Brownian motion. No other text covers all these advanced topics rigorously but at such an accessible level; all you need is calculus and material from a first undergraduate course in probability.
1101450509
A Second Course in Probability
The 2006 INFORMS Expository Writing Award-winning and best-selling author Sheldon Ross (University of Southern California) teams up with Erol Peköz (Boston University) to bring you this textbook for undergraduate and graduate students in statistics, mathematics, engineering, finance, and actuarial science. This is a guided tour designed to give familiarity with advanced topics in probability without having to wade through the exhaustive coverage of the classic advanced probability theory books. Topics include measure theory, limit theorems, bounding probabilities and expectations, coupling and Stein's method, martingales, Markov chains, renewal theory, and Brownian motion. No other text covers all these advanced topics rigorously but at such an accessible level; all you need is calculus and material from a first undergraduate course in probability.
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A Second Course in Probability

A Second Course in Probability

A Second Course in Probability

A Second Course in Probability

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Overview

The 2006 INFORMS Expository Writing Award-winning and best-selling author Sheldon Ross (University of Southern California) teams up with Erol Peköz (Boston University) to bring you this textbook for undergraduate and graduate students in statistics, mathematics, engineering, finance, and actuarial science. This is a guided tour designed to give familiarity with advanced topics in probability without having to wade through the exhaustive coverage of the classic advanced probability theory books. Topics include measure theory, limit theorems, bounding probabilities and expectations, coupling and Stein's method, martingales, Markov chains, renewal theory, and Brownian motion. No other text covers all these advanced topics rigorously but at such an accessible level; all you need is calculus and material from a first undergraduate course in probability.

Product Details

ISBN-13: 9780979570407
Publisher: Erol Pekoz
Publication date: 05/17/2007
Edition description: New Edition
Pages: 212
Product dimensions: 6.20(w) x 9.30(h) x 0.70(d)

About the Author

Sheldon M. Ross is the Epstein Chair Professor in the Epstein Department of Industrial and Systems Engineering at the University of Southern California. He has published more than 150 technical articles as well as a variety of textbooks in the areas of applied probability, statistics, and industrial engineering. He is the founding and continuing editor of the journal Probability in the Engineering and Informational Sciences, a fellow of the Institute of Mathematical Statistics and of the Institute for Operations Research and the Management Sciences, and a recipient of the Humboldt US Senior Scientist Award. He is the recipient of the 2006 INFORMS Expository Writing Award.

Erol A. Peköz is Professor and Department Chair of Operations and Technology Management in the Questrom School of Business at Boston University. He has published more than 50 technical articles in applied probability and statistics, and is the author of 'The Manager's Guide to Statistics' (2009). At Boston University, he was awarded the 2001 Broderick Prize for Teaching.

Table of Contents

Preface; 1. Measure Theory and Laws of Large Numbers; 2. Stein's Method and Central Limit Theorems; 3. Conditional Expectation and Martingales; 4. Bounding Probabilities and Expectations; 5. Markov Chains; 6. Renewal Theory; 7. Brownian Motion; References; Index.
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