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BAYESIAN TESTS AND MODEL DIAGNOSTICS IN CONDITIONALLY
 

Summary: BAYESIAN TESTS AND MODEL
DIAGNOSTICS IN CONDITIONALLY
INDEPENDENT HIERARCHICAL MODELS
Jim Albert \Lambda
Bowling Green State University, Bowling Green, USA
Siddhartha Chib
Washington University, St. Louis, USA
March, 1996
Abstract
Consider the conditionally independent hierarchical model (CIHM) where observa­
tions y i
are independently distributed from f(y i j` i
), the parameters ` i
are independently
distributed from distributions g(`j–), and the hyperparameters – are distributed accord­
ing to a distribution h(–). The posterior distribution of all parameters of the CIHM can
be efficiently simulated by Monte Carlo Markov Chain (MCMC) algorithms. Although
these simulation algorithms have facilitated the application of CIHM's, they generally
have not addressed the problem of computing quantities useful in model selection. This
paper explores how MCMC simulation algorithms and other related computational al­

  

Source: Albert, James H. - Department of Mathematics and Statistics, Bowling Green State University

 

Collections: Mathematics