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• W2046158133 endingPage "1467" @default.
• W2046158133 startingPage "1450" @default.
• W2046158133 abstract "A novel class of interacting Markov chain Monte Carlo (MCMC) algorithms, hereby referred to as the Parallel Hierarchical Sampler (PHS), is developed and its mixing properties are assessed. PHS algorithms are modular MCMC samplers designed to produce reliable estimates for multi-modal and heavy-tailed posterior distributions. As such, PHS aims at benefitting statisticians whom, working on a wide spectrum of applications, are more focused on defining and refining models than constructing sophisticated sampling strategies. Convergence of a vanilla PHS algorithm is proved for the case of Metropolis–Hastings within-chain updates. The accuracy of this PHS kernel is compared with that of optimized single-chain and multiple-chain MCMC algorithms for multi-modal mixtures of multivariate Gaussian densities and for ‘banana-shaped’ heavy-tailed multivariate distributions. These examples show that PHS can yield a dramatic improvement in the precision of MCMC estimators over standard samplers. PHS is then applied to two realistically complex Bayesian model uncertainty scenarios. First, PHS is used to select a low number of meaningful predictors for a Gaussian linear regression model in the presence of high collinearity. Second, the posterior probability of survival trees approximated by PHS indicates that the number and size of liver metastases at the time of diagnosis are predictive of substantial differences in the survival distributions of colorectal cancer patients." @default.
• W2046158133 created "2016-06-24" @default.
• W2046158133 creator A5030873840 @default.
• W2046158133 creator A5039784236 @default.
• W2046158133 date "2012-06-01" @default.
• W2046158133 modified "2023-10-16" @default.
• W2046158133 title "Parallel hierarchical sampling: A general-purpose interacting Markov chains Monte Carlo algorithm" @default.
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