Probability : theory and examples / Rick Durrett. 6
By: Durrett, Richard, 4 0 16 [, ] | [, ] |
Contributor(s): 5 6 [] |
Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; Cambridge series in statistical and probabilistic mathematicsCambridge : Cambridge University Press, ©201046Edition: Fourth editionDescription: 27 cm. x, 428 pages : illustrationsContent type: text Media type: unmediated Carrier type: volumeISBN: 9780521765398 (hardback)ISSN: 2Other title: 6 []Uniform titles: | | Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- Probabilities. -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | 519.2 D938p 2010 LOC classification: | QA273 | .D865 20102Other classification:| Item type | Current location | Home library | Collection | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|---|
| Book | PLM | PLM Circulation Section | Circulation-Circulating | 519.2 D938p 2010 (Browse shelf) | Available | C36831 |
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| 519.15195D227b Biostatistics : a foundation for analysis in the health sciences / | 519.1P199p Probability, random variables, and stochastic processes. | 519.2 B626p 2005 Probability demystified / | 519.2 D938p 2010 Probability : theory and examples / | 519.2 H859s 2011 Schaum's outlines probability, random variables & random processes / | 519.23N331s Stochastic modeling : analysis and simulation / | 519.2h369p Probability and stochastic processes for engineers / |
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Includes bibliographical references and index.
Measure theory -- Laws of large numbers -- Central limit theorems -- Random walks -- Martingales -- Markov chains -- Ergodic theorems -- Brownian motion -- Appendix A. Measure theory details.
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Provided by publisher. This book is an introduction to probability theory covering laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. It is a comprehensive treatment concentrating on the results that are the most useful for applications. Its philosophy is that the best way to learn probability is to see it in action, so there are 200 examples and 450 problems--
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