Improving power posterior estimation of statistical evidence

DOI10.1007/s11222-013-9397-1zbMath1322.62098arXiv1209.3198OpenAlexW3103263318MaRDI QIDQ746315

Jason Wyse, Nial Friel, Merrilee A. Hurn

Publication date: 16 October 2015

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1209.3198

zbMATH Keywords

thermodynamic integrationMarkov chain Monte Carlomarginal likelihoodstatistical evidencetemperingpower posteriorsstepping stone sampler

Mathematics Subject Classification ID

Bayesian inference (62F15)

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Uses Software

• tsbridge

Cites Work

• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Tuning tempered transitions
• Estimating Bayes factors via thermodynamic integration and population MCMC
• A path sampling identity for computing the Kullback-Leibler and J divergences
• Marginal Likelihood from the Gibbs Output
• Marginal Likelihood Estimation via Power Posteriors
• Accurate Approximations for Posterior Moments and Marginal Densities
• Nested sampling for general Bayesian computation
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