Bayesian Analysis of Realistically Complex Models

N. G. Best, D. J. Spiegelhalter, A. Thomas and C. E. G. Brayne

Journal of the Royal Statistical Society Series A, 1996, vol. 159, issue 2, 323-342

Abstract: Models with complex structure arise in many social science applications and appear natural candidates for the use of Markov chain Monte Carlo methods for inference. Conditional independence assumptions simplify the model specification and make estimation using Gibbs sampling particularly appropriate. Two examples are discussed: random effects models for repeated ordered categorical data and sensitivity analysis to assumptions concerning the mechanism underlying informative drop‐out in a longitudinal study. The use of a program bugs is demonstrated.

Date: 1996
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