Optimal scaling for the pseudo-marginal random walk Metropolis: insensitivity to the noise generating mechanism

DOI10.1007/S11009-015-9471-6zbMath1361.65003arXiv1408.4344OpenAlexW1852983166WikidataQ61434473 ScholiaQ61434473MaRDI QIDQ340131

Chris Sherlock

Publication date: 11 November 2016

Published in: Methodology and Computing in Applied Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1408.4344

zbMATH Keywords

robustnessimportance samplingoptimal scalingparticle Markov chain Monte Carlo methodpseudo marginal Markov chain Monte Carlorandom walk Metropolis

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Monte Carlo methods (65C05) Sums of independent random variables; random walks (60G50) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (3)

The node-wise pseudo-marginal method: model selection with spatial dependence on latent graphs ⋮ Bayesian computation: a summary of the current state, and samples backwards and forwards ⋮ Efficiency of delayed-acceptance random walk metropolis algorithms

Cites Work

• The pseudo-marginal approach for efficient Monte Carlo computations
• Establishing some order amongst exact approximations of MCMCs
• On some properties of Markov chain Monte Carlo simulation methods based on the particle filter
• Weak convergence and optimal scaling of random walk Metropolis algorithms
• Renewal theory and computable convergence rates for geometrically erdgodic Markov chains
• On the efficiency of pseudo-marginal random walk Metropolis algorithms
• The use of a single pseudo-sample in approximate Bayesian computation
• A lognormal central limit theorem for particle approximations of normalizing constants
• Bayesian inference for hybrid discrete-continuous stochastic kinetic models
• Particle Markov Chain Monte Carlo for Efficient Numerical Simulation
• Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator

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