Stochastic processes

By:

Ross, Sheldon M

Material type: Text

TextPublication details: 2014 Wiley New DelhiEdition: 2nd EdDescription: 23 cm ; Pbk xv, 510 pISBN:

978-8126517572

Subject(s):

Stochastic processes Statistics

DDC classification:

519.2 ROS

Contents:

1. Preliminaries 2. The Poisson Process 3. Renewal Theory 4. Markov Chains 5. Continuous-Time Markov Chains 6. Martingales 7. Random Walks 8. Brownian Motion and Other Markov Processes 9. Stochastic Order Relations 10. Poisson Approximations 11. Answers and Solutions to Selected Problems 12. Index

Summary: 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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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Books	H.T. Parekh Library	GSB Collection	519.2 ROS (Browse shelf(Opens below))	Available		37942

Gratis ₹.629.00/-

1. Preliminaries
2. The Poisson Process
3. Renewal Theory
4. Markov Chains
5. Continuous-Time Markov Chains
6. Martingales
7. Random Walks
8. Brownian Motion and Other Markov Processes
9. Stochastic Order Relations
10. Poisson Approximations
11. Answers and Solutions to Selected Problems
12. Index

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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