Description

This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians.

Key Features:

- Critical thinking on causal effects
- Objective Bayesian philosophy
- Nonparametric Bayesian methodology
- Simulation based computing techniques
- Bioinformatics and Biostatistics

Key Features

Key Features:

· Critical thinking on causal effects
· Objective Bayesian philosophy
· Nonparametric Bayesian methodology
· Simulation based computing techniques
· Bioinformatics and Biostatistics

Readership

Graduate students in Statistics, faculty and researchers interested in Bayesian philosophy and methodology, Scientists and Libraries.

Table of Contents

Preface Contributors 1. Bayesian Inference for Casual Effects (Donald B. Rubin) 2. Reference Analysis (Josè M. Bernardo) 3. Probability Matching Priors (Gauri Sankar Datta and Trevor J. Sweeting) 4. Model Selection and Hypothesis Testing Based on Objective Probabilities and Bayes Factors (Luis Raul Pericchi) 5. Role of P-values and other measures of evidence in Bayesian Analysis (Jayanta Ghosh, Sumitra Purkayastha and Tapas Samanta) 6. Bayesian Model Checking and Model Diagnostics (Hal S. Stern and Sandip Sinharay) 7. The Elimination of Nuisance Parameters (Brunero Liseo) 8. Bayesian Estimation of Multivariate Location Parameters (Ann Cohen Brandwein and William E. Strawdermann) 9. Bayesian Nonparametric Modeling and Data Analysis: An Introduction (Timothy E. Hanson, Adam J. Branscum and Wesley O. Johnson) 10. Some Bayesian Nonparametric Models (Paul Damien) 11. Bayesian Modeling in the Wavelet Domain (Fabrizio Ruggeri and Brani Vidakovic) 12. Bayesian Nonparametric Inference (Stephen Walker) 13. Bayesian Methods for Function Estimation (Nidhan Choudhuri, Subhashis Ghosal and Anindya Roy) 14. MCMC Methods to Estimate Bayesian Parametric Models (Antonietta Mira) 15. Bayesian Computation: From Posterior Densities to Bayes Factors, Marginal Likelihoods, and Posterior Model Probabilities (Ming-Hui Chen) 16. Bayesian Modelling and Inference on Mixtures of Distributions (Jean-Michel Marin, Kerrie Mengersen and Christian P. Robert) 17. Simulation Based Optimal Design (Peter Müller) 18. Variable Selection and Covariance Selection in Multivariate Regression Models (Edward Cripps, Chris Carter and Robert Kohn) 19. Dynamic Models (Helio S. Mignon, Dani Gamerman, Hedibert F. Lopes and Marco A.R. Ferreira)

Details

No. of pages:
1062
Language:
English
Copyright:
© 2005
Published:
Imprint:
North Holland
Electronic ISBN:
9780080461175
Print ISBN:
9780444515391