Automated parameter blocking for efficient Markov chain Monte Carlo sampling

DOI10.1214/16-BA1008zbMath1384.62022arXiv1503.05621OpenAlexW2963636084MaRDI QIDQ1699668

Christopher J. Paciorek, Daniel Turek, Perry de Valpine, Clifford Anderson-Bergman

Publication date: 23 February 2018

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1503.05621

zbMATH Keywords

mixingMCMCMetropolis-Hastingsblock samplingintegrated autocorrelation timeNIMBLE

Mathematics Subject Classification ID

Numerical analysis or methods applied to Markov chains (65C40) General considerations in statistical decision theory (62C05)

Related Items (6)

On the convergence rate of the ``out-of-order block Gibbs sampler ⋮ Demand Models With Random Partitions ⋮ Nested adaptation of MCMC algorithms ⋮ Spatiotemporal blocking of the bouncy particle sampler for efficient inference in state-space models ⋮ Simulating Markov Random Fields With a Conclique-Based Gibbs Sampler ⋮ Block Gibbs samplers for logistic mixed models: convergence properties and a comparison with full Gibbs samplers

Uses Software

• Unnamed Item
• WinBUGS
• Eigen
• BUGS
• JAGS
• Stan
• nimble

This page was built for publication: Automated parameter blocking for efficient Markov chain Monte Carlo sampling