Markov processes are used to model systems with limited memory. They are used in many areas including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, popula
Markov Processes for Stochastic Modeling
β Scribed by Oliver Ibe (Auth.)
- Year
- 2013
- Tongue
- English
- Leaves
- 492
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Table of Contents
Content:
Front-matter, Pages i,iii
Copyright, Page iv
Acknowledgments, Page xv
Preface to the Second Edition, Pages xvii-xviii
Preface to the First Edition, Page xix
1 - Basic Concepts in Probability, Pages 1-27
2 - Basic Concepts in Stochastic Processes, Pages 29-48
3 - Introduction to Markov Processes, Pages 49-57
4 - Discrete-Time Markov Chains, Pages 59-84
5 - Continuous-Time Markov Chains, Pages 85-102
6 - Markov Renewal Processes, Pages 103-143
7 - Markovian Queueing Systems, Pages 145-203
8 - Random Walk, Pages 205-261
9 - Brownian Motion, Pages 263-293
10 - Diffusion Processes, Pages 295-327
11 - Levy Processes, Pages 329-347
12 - Markovian Arrival Processes, Pages 349-376
13 - Controlled Markov Processes, Pages 377-416
14 - Hidden Markov Models, Pages 417-451
15 - Markov Point Processes, Pages 453-480
References, Pages 481-494
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