Optimal scaling of random walk Metropolis algorithms with discontinuous target densities

DOI10.1214/11-AAP817zbMath1259.60082arXiv1210.5090OpenAlexW3101802995MaRDI QIDQ691108

Peter Neal, Wai Kong Yuen, Gareth O. Roberts

Publication date: 29 November 2012

Published in: The Annals of Applied Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1210.5090

zbMATH Keywords

Markov chain Monte Carlorandom walk Metropolis algorithmoptimal scalingdiscontinuous densities

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Central limit and other weak theorems (60F05) Monte Carlo methods (65C05) Discrete-time Markov processes on general state spaces (60J05) Numerical analysis or methods applied to Markov chains (65C40)

Related Items

Automatically tuned general-purpose MCMC via new adaptive diagnostics, Complexity bounds for Markov chain Monte Carlo algorithms via diffusion limits, A Dirichlet form approach to MCMC optimal scaling, Dimension-Independent MCMC Sampling for Inverse Problems with Non-Gaussian Priors, Sampling Constrained Probability Distributions Using Spherical Augmentation, Optimal scaling of random-walk Metropolis algorithms on general target distributions, Collapsing of Non‐centred Parameterized MCMC Algorithms with Applications to Epidemic Models, Parallel hierarchical sampling: a general-purpose interacting Markov chains Monte Carlo algorithm, Comparison of hit-and-run, slice sampler and random walk Metropolis, Optimal scaling for the transient phase of Metropolis Hastings algorithms: the longtime behavior, Counterexamples for optimal scaling of Metropolis-Hastings chains with rough target densities, Optimal scaling of the random walk Metropolis algorithm under L^p mean differentiability, Bayesian computation: a summary of the current state, and samples backwards and forwards, Random walk Metropolis algorithm in high dimension with non-Gaussian target distributions, Optimal scaling of Metropolis algorithms: Heading toward general target distributions, Forward Simulation Markov Chain Monte Carlo with Applications to Stochastic Epidemic Models, Optimal scaling for the transient phase of the random walk Metropolis algorithm: the mean-field limit

Cites Work

• Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets
• Optimal scaling of random walk Metropolis algorithms with non-Gaussian proposals
• Optimal scaling for random walk Metropolis on spherically constrained target densities
• Optimal scaling for partially updating MCMC algorithms
• Weak convergence and optimal scaling of random walk Metropolis algorithms
• Optimal scaling for various Metropolis-Hastings algorithms.
• From Metropolis to diffusions: Gibbs states and optimal scaling.
• Weak convergence of Metropolis algorithms for non-I.I.D. target distributions
• Optimal acceptance rates for Metropolis algorithms: Moving beyond 0.234
• Scaling Limits for the Transient Phase of Local Metropolis–Hastings Algorithms
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item