1. Introduction to Bayesian Inference for Stochastic Processes
2. Bayesian Analysis
3. Introduction to Stochastic Processes
4. Bayesian Inference for Discrete Markov Chains
5. Examples of Markov Chains in Biology
6. Inferences for Markov Chains in Continuous Time
7. Bayesian Inference: Examples of Continuous-Time Markov Chains
8. Bayesian Inferences for Normal Processes
9. Queues and Time Series
Biography
Lyle D. Broemeling, Ph.D., is Director of Broemeling and Associates Inc., and is a consulting biostatistician. He has been involved with academic health science centers for about 20 years and has taught and been a consultant at the University of Texas Medical Branch in Galveston, The University of Texas MD Anderson Cancer Center and the University of Texas School of Public Health. His main interest is in developing Bayesian methods for use in medical and biological problems and in authoring textbooks in statistics. His previous books are Bayesian Biostatistics and Diagnostic Medicine, and Bayesian Methods for Agreement
"Readers with a good background in the two areas, probability theory and statistical inference, should be able to master the essential ideas of this book."~ Ludwig Paditz, Dresden
". . .All three important types of Bayesian inferences such are estimation, hypothesis testing and forecasting are considered and many examples are worked through using R and WinBUGS codes. . . It will prove useful for students and scientists who want to learn about Bayesian analysis in stochastic processes." ~Miroslav M. Ristic, Stat Papers
