Nested adaptation of MCMC algorithms

DOI10.1214/19-BA1190zbMath1480.62180arXiv1802.08798OpenAlexW2994552149MaRDI QIDQ2057363

Yanyan Li

Publication date: 6 December 2021

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

Full work available at URL: https://arxiv.org/abs/1802.08798

zbMATH Keywords

mixingadaptive MCMCMCMC efficiencyintegrated autocorrelation time

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)

Uses Software

• R
• CODA
• WinBUGS
• Eigen
• GMRFLib
• spBayes
• nimble

Cites Work

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• Limit theorems for some adaptive MCMC algorithms with subgeometric kernels
• On adaptive Markov chain Monte Carlo algorithms
• Optimal scaling for various Metropolis-Hastings algorithms.
• Self-regenerative Markov chain Monte Carlo with adaptation
• Automated parameter blocking for efficient Markov chain Monte Carlo sampling
• On the efficiency of adaptive MCMC algorithms
• Componentwise adaptation for high dimensional MCMC
• Bayes Empirical Bayes
• Adaptive Markov Chain Monte Carlo through Regeneration
• Gaussian Markov Random Fields
• Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms
• An adaptive Metropolis algorithm

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