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An overview of modelling applications from a Bayesian perspective including a range of methodolgies and modelling techniques. Markov Chain and Monte Carlo methods are covered. Worked examples from the health and social sciences are presented.

This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBUGS and OPENBUGS. This feature continues in the new edition along with examples using R to broaden appeal and for completeness of coverage. Peter Congdon is a quantitative geographer with particular interests in geographic epidemiology, application of spatial statistical methods to area health and health survey data, and spatial demography. Since 2001 he has been a Research Professor in the School of Geography, and am also affiliated to the QMUL Life Sciences Institute. Queen Mary College, Unviersity of London, England. Publisher's note.