Optimal Scaling of the Random Walk Metropolis: General Criteria for the 0.234 Acceptance Rule

DOI10.1239/jap/1363784420zbMath1266.60062OpenAlexW1968152375WikidataQ61434489 ScholiaQ61434489MaRDI QIDQ4918557

Chris Sherlock

Publication date: 25 April 2013

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

Full work available at URL: https://projecteuclid.org/euclid.jap/1363784420

zbMATH Keywords

optimal scalingrandom walk Metropolisoptimal acceptance rate

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Functional limit theorems; invariance principles (60F17)

Related Items (4)

Information-geometric Markov chain Monte Carlo methods using diffusions ⋮ Efficiency of delayed-acceptance random walk metropolis algorithms ⋮ A class of spherical and elliptical distributions with Gaussian-like limit properties ⋮ On the efficiency of pseudo-marginal random walk Metropolis algorithms

Cites Work

• Unnamed Item
• Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets
• Towards optimal scaling of Metropolis-coupled Markov chain Monte Carlo
• A class of spherical and elliptical distributions with Gaussian-like limit properties
• Optimal scaling for random walk Metropolis on spherically constrained target densities
• 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.
• Optimal scalings for local Metropolis-Hastings chains on nonproduct targets in high dimensions
• Weak convergence of Metropolis algorithms for non-I.I.D. target distributions
• Optimal acceptance rates for Metropolis algorithms: Moving beyond 0.234
• Optimal metropolis algorithms for product measures on the vertices of a hypercube

This page was built for publication: Optimal Scaling of the Random Walk Metropolis: General Criteria for the 0.234 Acceptance Rule