Stochastic processes
Material type: TextPublication details: New Delhi Wiley India Pvt. Ltd. 2014 Edition: 2ndDescription: 510 pISBN: 9788126517572Subject(s): Stochastic processesDDC classification: 519.2 Summary: Description The book provides a non measure theoretic introduction to stochastic processes, probabilistic intuition and insight in thinking about problems. This revised edition contains additional material on compound Poisson random variables including an identity which can be used to efficiently compute moments, Poisson approximations; and coverage of the mean time spent in transient states as well as examples relating to the Gibb's sampler, the Metropolis algorithm and mean cover time in star graphs.Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
---|---|---|---|---|---|---|---|
Book | Indian Institute of Management LRC General Stacks | Operations Management & Quantitative Techniques | 519.2 ROS (Browse shelf(Opens below)) | 1 | Available | 000586 |
Browsing Indian Institute of Management LRC shelves, Shelving location: General Stacks, Collection: Operations Management & Quantitative Techniques Close shelf browser (Hides shelf browser)
519.2 HOG Probability and statistical inference | 519.2 KUL Introduction to modeling and analysis of stochastic systems | 519.2 LIP Schaum's outline of theory and problems of introduction to probability and statistics | 519.2 ROS Stochastic processes | 519.2 ROS A first course in probability | 519.2 RUD Probability theory: a primer | 519.23 OLO Probability, statistics, and stochastic processes |
Table of Content
Preliminaries
· The Poisson Process
· Renewal Theory
· Markov Chains
· Continuous-Time Markov Chains
· Martingales
· Random Walks
· Brownian Motion and Other Markov Processes
· Stochastic Order Relations
· Poisson Approximations
Answers and Solutions to Selected Problems
Index
Description
The book provides a non measure theoretic introduction to stochastic processes, probabilistic intuition and insight in thinking about problems. This revised edition contains additional material on compound Poisson random variables including an identity which can be used to efficiently compute moments, Poisson approximations; and coverage of the mean time spent in transient states as well as examples relating to the Gibb's sampler, the Metropolis algorithm and mean cover time in star graphs.
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