Bayesian statistical analyses have become increasingly common over the last two decades. The rapid increase in computing power that facilitated their implementation coincided with major changes in the research interests of, and data availability for, social scientists. Specifically, the last two decades have seen an increase in the availability of panel data sets, other hierarchically structured data sets including spatially organized data, along with interests in life course processes and the influence of context on individual behavior and outcomes. The Bayesian approach to statistics is well-suited for these types of data and research questions. Applied Bayesian Statistics is an introduction to these methods that is geared toward social scientists. Author Scott M. Lynch makes the material accessible by emphasizing application more than theory, explaining the math in a step-by-step fashion, and demonstrating the Bayesian approach in analyses of U.S. political trends drawing on data from the General Social Survey.
Tabella dei contenuti
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
Circa l’autore
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.
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