The BUGS Book: A Practical Introduction to Bayesian Analysis (Chapman & Hall/CRC Texts in Statistical Science)

David Lunn, Chris Jackson, Nicky Best, Andrew Thomas, David Spiegelhalter

ISBN 10: 1584888490 / ISBN 13: 9781584888499

Published by Chapman and Hall/CRC 2012-11-01, 2012

About this Item

Seller Inventory # 6666-GRD-9781584888499

Bibliographic Details

• Title: The BUGS Book: A Practical Introduction to Bayesian Analysis (Chapman & Hall/CRC Texts in Statistical Science)
• Publisher: Chapman and Hall/CRC 2012-11-01
• Publication Date: 2012
• Language: English
• ISBN 10: 1584888490
• ISBN 13: 9781584888499
• Binding: Paperback
• Condition: New

About this title

Synopsis

Bayesian statistical methods have become widely used for data analysis and modelling in recent years, and the BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software, The BUGS Book provides a practical introduction to this program and its use. The text presents complete coverage of all the functionalities of BUGS, including prediction, missing data, model criticism, and prior sensitivity. It also features a large number of worked examples and a wide range of applications from various disciplines.

The book introduces regression models, techniques for criticism and comparison, and a wide range of modelling issues before going into the vital area of hierarchical models, one of the most common applications of Bayesian methods. It deals with essentials of modelling without getting bogged down in complexity. The book emphasises model criticism, model comparison, sensitivity analysis to alternative priors, and thoughtful choice of prior distributions-all those aspects of the "art" of modelling that are easily overlooked in more theoretical expositions.

More pragmatic than ideological, the authors systematically work through the large range of "tricks" that reveal the real power of the BUGS software, for example, dealing with missing data, censoring, grouped data, prediction, ranking, parameter constraints, and so on. Many of the examples are biostatistical, but they do not require domain knowledge and are generalisable to a wide range of other application areas.

Full code and data for examples, exercises, and some solutions can be found on the book's website.

About the Author

MRC Biostatistics Unit, Cambridge, UK Imperial College, London, UK Helsinki, Finland College School of Medicine, London, UK University of Bath, UK University of Minnesota, Minneapolis, Minnesota, USA Northwestern University, Evanston, Illinois, USA University of British Columbia, Vancouver, Canada