The second edition of this popular text explores advanced topics in probability while keeping mathematical prerequisites to a minimum. With copious exercises and examples, it is an ideal guide for graduate students and professionals in application domains that depend on probability, including operations research, finance and machine learning.
The second edition of this popular text explores advanced topics in probability while keeping mathematical prerequisites to a minimum. With copious exercises and examples, it is an ideal guide for graduate students and professionals in application domains that depend on probability, including operations research, finance and machine learning.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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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