Elements of Applied Stochastic Processes, 3rd Edition
- Integration of theory and application offers improved teachability
- Provides a comprehensive introduction to stationary processes and time series analysis
- Integrates a broad set of applications into the text
- Utilizes a wealth of examples from research papers and monographs
Stochastic Processes: Description and Definition.
Irreducible Markov Chains with Ergodic States.
Branching Processes and Other Special Topics.
Statistical Inference for Markov Chains.
Applied Markov Chains.
Simple Markov Processes.
Statistical Inference for Simple Markov Processes.
Applied Markov Processes.
Stationary Processes and Time Series Analysis.
Simulation and Markov Chain Monte Carlo.
Answers to Selected Exercises.
GREGORY K. MILLER, PhD, is Associate Professor of Statistics at Stephen F. Austin State University.
"...an extended and well-written introduction to the theory...of stochastic processes and their applications..." (Zentralblatt Math, Vol. 1024, 2004)
"...besides conveying the concepts of stochastic processes, this book succeeds in providing insight into the reasons why for a particular topic certain lines of investigation are pursued and why certain variables/functions are introduced." (Technometrics, Vol. 45, No. 3, August 2003)