Merging MCMC subposteriors through Gaussian-process approximations

DOI10.1214/17-BA1063zbMath1407.62081arXiv1605.08576WikidataQ61434463 ScholiaQ61434463MaRDI QIDQ1631561

Chris Sherlock, Christopher Nemeth

Publication date: 6 December 2018

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

Full work available at URL: https://arxiv.org/abs/1605.08576

zbMATH Keywords

Gaussian processesMarkov chain Monte Carlobig datadistributed importance sampling

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Bayesian inference (62F15) Monte Carlo methods (65C05)

Related Items (9)

Unnamed Item ⋮ Challenges in Markov chain Monte Carlo for Bayesian neural networks ⋮ Reflections on Bayesian inference and Markov chain Monte Carlo ⋮ Divide-and-conquer Metropolis-Hastings samplers with matched samples ⋮ Speeding up MCMC by Delayed Acceptance and Data Subsampling ⋮ Speeding Up MCMC by Efficient Data Subsampling ⋮ Subsampling MCMC -- an introduction for the survey statistician ⋮ Distributed Bayesian matrix factorization with limited communication ⋮ Stochastic Gradient Markov Chain Monte Carlo

Uses Software

• Stan
• NUTS

Cites Work

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• The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
• Weak convergence and optimal scaling of random walk Metropolis algorithms
• Nonparametric density estimation with a parametric start
• Optimal tuning of the hybrid Monte Carlo algorithm
• Sparse On-Line Gaussian Processes
• Optimal Scaling of Discrete Approximations to Langevin Diffusions
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• Bayesian Inference in Econometric Models Using Monte Carlo Integration

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