PRIOR SPECIFICATION IN BAYESIAN ESTIMATION WHEN FITTING DIRICHLET PROCESS LOGNORMAL MIXTURE MODELS
Abstract
In this paper Prior specification on Bayesian inference when fitting a Dirichlet process mixture model with a lognormal kernel (DPLNMM) in the presence of censoring is considered. We study the effects of prior choices on posterior inference by varying prior dispersion. Simulation and an application to leukemia data study was carried out with different priors. The Bayesian approach via the Gibbs Sampler Markov Chain Monte Carlo algorith.
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