TY - JOUR
AU - Brayne, C. E. G.
AB - SummaryModels 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.
TI - Bayesian Analysis of Realistically Complex Models
JF - Journal of the Royal Statistical Society Series A (Statistics in Society)
DO - 10.2307/2983178
DA - 2018-12-05
UR - https://www.deepdyve.com/lp/oxford-university-press/bayesian-analysis-of-realistically-complex-models-qTf2WDNfDR
SP - 323
EP - 342
VL - 159
IS - 2
DP - DeepDyve
ER -