Sheldon M. Ross, Erol A. Pekoz

Second Course in Probability (eBook, PDF)

• Format: PDF

Produktbeschreibung

Written by Sheldon Ross and Erol Pekoz, this text familiarises you with advanced topics in probability while keeping the mathematical prerequisites to a minimum. Topics covered 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 topics rigorously but at such an accessible level - all you need is an undergraduate-level understanding of calculus and probability. New to this edition are sections on the gambler's ruin problem, Stein's method as applied to exponential approximations, and applications of the martingale stopping theorem. Extra end-of-chapter exercises have also been added, with selected solutions available.This is an ideal textbook for students taking an advanced undergraduate or graduate course in probability. It also represents a useful resource for professionals in relevant application domains, from finance to machine learning.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 21. September 2023
• Englisch
• ISBN-13: 9781009189231
• Artikelnr.: 70912816

Autorenporträt

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.

Inhaltsangabe

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.