1. Introduction
2. Probability Distributions and Review of Classical Analysis
3. The Bayesian Approach to Probability and Statistics
4. Markov Chain Monte Carlo (MCMC) Sampling Methods
5. Implementing the Bayesian Approach in Realistic Applications
6. Conclusion

Applied Bayesian Statistics provides a broad, but in-depth introduction to Bayesian statistics, both in terms of its basic theoretical underpinnings and its contemporary methods of application. The book is highly applied-more of a "how to" guide-with statistical theory limited to what is needed to understand the basic ideas. The focus is on common models used by social scientists, and extensions to them that the Bayesian approach facilitates. The author uses publicly-accessible and user-friendly datasets for the examples, such as the General Social Survey data.

Scott M. Lynch is a professor in the departments of Sociology and Family Medicine and Community Health at Duke University. He is a demographer, statistician, and social epidemiologist and is currently the director of the Center for Population Health and Aging in Duke's Population Research Institute, where he is the associate director. His main substantive interests are in life course and cohort patterns in socioeconomic, racial, and regional dis-parities in health and mortality in the U.S. His main statistical interests are in the use of Bayesian statistics in social science and demographic research, especially in survival and life table methods. He has published more than 60 articles and chapters in these areas in top demography, gerontology, methodology, sociology and other journals, as well as two prior statistics texts on Bayesian methods and introductory statistics. He has taught undergraduate and graduate level statistics courses on a variety of statistical methods at Princeton University and Duke University, as well as a number of seminars on Bayesian statistics in academic, business, and other venues.