Bayesian Statistical Modelling

Abstract

Peter Congdon's Bayesian Statistical Modelling is not a teaching textbook or introduction to Bayesian statistical modelling. Although the basics of Bayesian theory and Markov Chain Monte Carlo (MCMC) methods are briefly reviewed in the book, I think that one should already be familiar with those topics before using the book. Given that, the book can be very helpful to an applied statistician, as it is an excellent reference source for Bayesian models and literature. Using nearly 200 worked examples with data examples and computer code available via the World Wide Web, the book reviews a large number of models, e.g., for standard distributions, classification, regression, hierarchical pooling of information, missing data, correlated data, multivariate data, time series, spatial data, longitudinal data, measurement error, life table and survival analysis. Each chapter starts with an introduction to the model family and then continues with describing variations to basic models, with advice as to model identification, prior selection, interpretation of findings, and computing choices and strategies. In the last chapter also the Bayesian model assessment is briefly reviewed. With 500 pages in the book, there are about 2.5 pages per example, and consequently I believe that in most cases it would be necessary to read also some of the references in order to fully benefit from the models described. Although the data examples are mainly from medical science, public health and the social sciences, the book should be interesting to any applied statistician seeking new possibilities in data analysis. Aki Vehtari