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Probability and stochastic processes / Ionut Florescu.

By: Material type: TextTextPublication details: New Jersey : John Wiley, 2015Description: xxiii, 551 p. : illustrations ; 25 cmISBN:
  • 9780470624555 (cloth)
Subject(s): DDC classification:
  • 519.2 23 F634
Contents:
1. Elements of probability measure-- 2. Random variables-- 3. Applied chapter: Generating random variables-- 4. Integration theory-- 5. Conditional distribution and conditional expectation-- 6. Moment generating function. characteristic function-- 7. Limit theorems-- 8. Statistical inference-- 9. Introduction to stochastic processes-- 10. The poisson process-- 11. Renewal processes-- 12. Markov chains-- 13. Semi-Markov and continuous-time Markov processes-- 14. Martingales-- 15. Brownian motion-- 16. Stochastic differential equations-- A. Appendix: Linear algebra and solving difference equations and systems of differential equations-- Bibliography-- Index.
Summary: The book s primary focus is on key theoretical notions in probability to provide a foundation for understanding concepts and examples related to stochastic processes. Organized into two main sections, the book begins by developing probability theory with topical coverage on probability measure; random variables; integration theory; product spaces, conditional distribution, and conditional expectations; and limit theorems. The second part explores stochastic processes and related concepts including the Poisson process, renewal processes, Markov chains, semi–Markov processes, martingales, and Brownian motion. Featuring a logical combination of traditional and complex theories as well as practices, Probability and Stochastic Processes also includes.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 519.2 F634 (Browse shelf(Opens below)) Available 135965
Total holds: 0

Includes bibliographical references and index.

1. Elements of probability measure--
2. Random variables--
3. Applied chapter: Generating random variables--
4. Integration theory--
5. Conditional distribution and conditional expectation--
6. Moment generating function. characteristic function--
7. Limit theorems--
8. Statistical inference--
9. Introduction to stochastic processes--
10. The poisson process--
11. Renewal processes--
12. Markov chains--
13. Semi-Markov and continuous-time Markov processes--
14. Martingales--
15. Brownian motion--
16. Stochastic differential equations--
A. Appendix: Linear algebra and solving difference equations and systems of differential equations--
Bibliography--
Index.


The book s primary focus is on key theoretical notions in probability to provide a foundation for understanding concepts and examples related to stochastic processes.
Organized into two main sections, the book begins by developing probability theory with topical coverage on probability measure; random variables; integration theory; product spaces, conditional distribution, and conditional expectations; and limit theorems. The second part explores stochastic processes and related concepts including the Poisson process, renewal processes, Markov chains, semi–Markov processes, martingales, and Brownian motion. Featuring a logical combination of traditional and complex theories as well as practices, Probability and Stochastic Processes also includes.

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