Bayesian Statistics: Principles, Models, and Applications

An introduction to Bayesian statistics, with emphasis on interpretation of theory, and application of Bayesian ideas to practical problems, is provided in this volume. The first part covers basic issues and principles, such as subjective probability, Bayesian inference and decision making, the likelihood principle, predictivism, and numerical methods of approximating posterior distributions, and includes a listing of Bayesian computer programs. The second part is devoted to models and applications, including univariate and multivariate regression models, the general linear model, Bayesian classification and discrimination, and a case study of how disputed authorship of some of the Federalist Papers was resolved via Bayesian analysis. The book also includes biographical material on Thomas Bayes, and a reproduction of Bayes's original essay.