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Showing 1 to 4 of 4 for “"Dirichlet Process Priors"”.
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Using Dirichlet Process Priors For Bayesian Mixture Clustering
… that follow multinomial distributions. The Dirichlet Process is applied as the prior distribution. The method estimates the number of populations together with the allele frequencies and the ancestry coefficients of each individual. Distance matrices and bootstrap support numbers based on …
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Semiparametric Bayesian Joint Model With Variable Selection
… and survival data are modeled jointly. Dirichlet process priors are used to relax the parametric assumption of random effects, which has advantages of making the model more robust against possible misspecifications and allows the clustering of subjects. A fully Bayesian method for subset …
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Semiparametric Varying Coefficient Models for Matched Case-Crossover Studies
… nonparametric Bayesian approach constructed with Dirichlet process priors, which clusters subpopulations and assesses heterogeneity. We demonstrate the accuracy of our approach using a simulation study, as well a an example of a 1-4 bi-directional case-crossover study.
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Random Effects Selection In Bayesian Accelerated Failure Time Model With Correlated Interval Censored Data
… method for the selection of random effects. The Dirichlet prior is used to model the uncertainty in the random effects. The error distribution for the AFT model has been specified using a Gaussian mixture to allow flexible error density and prediction of the survival and hazard functions. We …