Bayesian Statistical Modelling

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John Wiley & Sons, Apr 4, 2007 - Mathematics - 596 pages
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Bayesian methods combine the evidence from the data at hand withprevious quantitative knowledge to analyse practical problems in awide range of areas. The calculations were previously complex, butit is now possible to routinely apply Bayesian methods due toadvances in computing technology and the use of new samplingmethods for estimating parameters. Such developments together withthe availability of freeware such as WINBUGS and R have facilitateda rapid growth in the use of Bayesian methods, allowing theirapplication in many scientific disciplines, including appliedstatistics, public health research, medical science, the socialsciences and economics.

Following the success of the first edition, this reworked andupdated book provides an accessible approach to Bayesian computingand analysis, with an emphasis on the principles of priorselection, identification and the interpretation of real datasets.

The second edition:

  • Provides an integrated presentation of theory, examples,applications and computer algorithms.
  • Discusses the role of Markov Chain Monte Carlo methods incomputing and estimation.
  • Includes a wide range of interdisciplinary applications, and alarge selection of worked examples from the health and socialsciences.
  • Features a comprehensive range of methodologies and modellingtechniques, and examines model fitting in practice using Bayesianprinciples.
  • Provides exercises designed to help reinforce thereader’s knowledge and a supplementary website containingdata sets and relevant programs.

Bayesian Statistical Modelling is ideal for researchersin applied statistics, medical science, public health and thesocial sciences, who will benefit greatly from the examples andapplications featured. The book will also appeal to graduatestudents of applied statistics, data analysis and Bayesian methods,and will provide a great source of reference for both researchersand students.

Praise for the First Edition:

“It is a remarkable achievement to have carried out such arange of analysis on such a range of data sets. I found this bookcomprehensive and stimulating, and was thoroughly impressed withboth the depth and the range of the discussions it contains.”– ISI - Short Book Reviews

“This is an excellent introductory book on Bayesianmodelling techniques and data analysis” –Biometrics

“The book fills an important niche in the statisticalliterature and should be a very valuable resource for students andprofessionals who are utilizing Bayesian methods.” –Journal of Mathematical Psychology


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Chapter 1 Introduction The Bayesian Method its Benefits and Implementation
Chapter 2 Bayesian Model Choice Comparison and Checking
Chapter 3 The Major Densities and their Application
Chapter 4 Normal Linear Regression General Linear Models and LogLinear Models
Chapter 5 Hierarchical Priors for Pooling Strength and Overdispersed Regression Modelling
Chapter 6 Discrete Mixture Priors
Chapter 7 Multinomial and Ordinal Regression Models
Chapter 8 Time Series Models
Chapter 11 Multilevel and Panel Data Models
Chapter 12 Latent Variable and Structural Equation Models for Multivariate Data
Chapter 13 Survival and Event History Analysis
Chapter 14 Missing Data Models
Chapter 15 Measurement Error Seemingly Unrelated Regressions and Simultaneous Equations
Appendix 1 A Brief Guide to Using WINBUGS
Wiley Series in Probability and Statistics

Chapter 9 Modelling Spatial Dependencies
Chapter 10 Nonlinear and Nonparametric Regression

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About the author (2007)

Peter Congdon is Research Professor of Quantitative Geography and Health Statistics at Queen Mary University of London. He has written three earlier books on Bayesian modelling and data analysis techniques with Wiley, and has a wide range of publications in statistical methodology and in application areas. His current interests include applications to spatial and survey data relating to health status and health service research. His recent publications include work associated with the British Historical GIS Project (University of Portsmouth) and international collaborative work on psychiatric admissions in London and New York.

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