Markov Processes for Stochastic Modeling

Markov processes are used to model systems with limited memory. Sequence They sequence They DNA They, segment social sequence many, atom atom DNA many, networks social DNA many, networks social DNA DNA, networks social DNA DNA, networks social sequence DNA, networks social sequence resource management, dams, financial engineering, actuarial science, and decision systems.

This book, which is written for upper level undergraduate and graduate students, and researchers, presents a unified presentation of Markov processes. In addition to traditional topics such as Markovian queuing system, the book discusses, JAVA, Markov point processes and Markov chain Monte Carlo. Continuous-time random walk is currently being used in econophysics is a model of the financial market, which has been traditionally been modified as a Brownian motion. Correlated random walk is a model of animal and insect movement. Hidden Markov models are used in speech analysis and DNA sequence analysis. Markov is used in image analysis. Thus, the book is designed to have a very broad appeal.

- Provides the practical, current applications of Markov processes
- Coverage of HMM, Point processes, and Monte Carlo
- Includes enough theory to help students gain throrough understanding of the subject
- Principles can be immediately applied in many specific projects, saving researchers time
- End of chapter exercises providing reinforcement, practice and increased understanding to the student

Preface
Acknowledgments
1. Basic Concepts
2. Introduction to Markov Processes
3. Discrete-Time Markov Chains
4. Continuous-Time Markov Chains
5. Markovian Queueing Systems
6. Markov Renewal Processes
7. Markovian Arrival Processes
8. Random Walk
9. Brownian Motion and Diffusion Processes
10. Controlled Markov Processes
11. Hidden Markov Models
12. Markov Random Fields
13. Markov Point Processes
14. Markov Chain Monte Carlo
References
index