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Markov chain Monte Carlo (MCMC) methods make possible the use of exible Bayesian models that would otherwise be computationally infeasible. In recent years, a great variety of such applications have been described in the literature. Applied statisticians who are new to these methods may have several questions and concerns, however: How much eeort and(More)
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The authors consider the problem of Bayesian variable selection for proportional hazards regression models with right censored data. They propose a semi-parametric approach in which a nonparametric prior is specified for the baseline hazard rate and a fully para-metric prior is specified for the regression coefficients. For the baseline hazard, they use a(More)
JS 3/16, derived from passaged oligodendroglial cultures prepared from rat cerebral white matter, differentiate from progenitors (OP) into complex process-bearing, galactocerebroside-positive but myelin basic protein-negative immature oligodendrocyte-like cells (ImO) after withdrawal of trophic factors. We found that JS 3/16 ImO are markedly more(More)
In Bayesian inference, a Bayes factor is deened as the ratio of posterior odds versus prior odds where posterior odds is simply a ratio of the normalizing constants of two posterior densities. In many practical problems, the two posteriors have diierent dimensions. For such cases, the current Monte Carlo methods such as the bridge sampling method (Meng and(More)
This article presents a Bayesian method for the analysis of toxicological multivariate mortality data when the discrete mortality rate for each family of subjects at a given time depends on familial random eeects and the toxic level experienced by the family. Our aim is to model and analyze one of such multivariate mortality data with large family sizes,(More)
Caspase-3 enzyme activity is induced, and cell death follows, when cerebellar granule neurons (CGNs) from 8-day-old rats are transferred from an extracellular concentration of 25 mM K+ (25 mM [K+]e) to 5 mM [K+]e. Death of these neurons is diminished by an inhibitor of caspase-3 but not by an inhibitor of caspase-1. Actinomycin D and cycloheximide inhibit(More)
Bayesian phylogenetic analyses often depend on Bayes factors (BFs) to determine the optimal way to partition the data. The marginal likelihoods used to compute BFs, in turn, are most commonly estimated using the harmonic mean (HM) method, which has been shown to be inaccurate. We describe a new more accurate method for estimating the marginal likelihood of(More)