Optimal scaling for the transient phase of Metropolis Hastings algorithms: the longtime behavior

DOI10.3150/13-BEJ546zbMath1329.60261arXiv1212.5517OpenAlexW4300895075MaRDI QIDQ470057

Benjamin Jourdain, Tony Lelièvre, Błażej Miasojedow

Publication date: 11 November 2014

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1212.5517

zbMATH Keywords

random walkpropagation of chaosoptimal scalingdiffusion limitsMetropolis Hastings algorithms

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Sums of independent random variables; random walks (60G50) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Numerical analysis or methods applied to Markov chains (65C40) Diffusion processes (60J60) Functional limit theorems; invariance principles (60F17)

Related Items (12)

Complexity bounds for Markov chain Monte Carlo algorithms via diffusion limits ⋮ A Dirichlet form approach to MCMC optimal scaling ⋮ Optimal scaling of the MALA algorithm with irreversible proposals for Gaussian targets ⋮ Optimal scaling of random-walk Metropolis algorithms on general target distributions ⋮ Optimal scaling of MCMC beyond Metropolis ⋮ Counterexamples for optimal scaling of Metropolis-Hastings chains with rough target densities ⋮ Bayesian computation: a summary of the current state, and samples backwards and forwards ⋮ Non-stationary phase of the MALA algorithm ⋮ Propagation of chaos: a review of models, methods and applications. II: Applications ⋮ Diffusion limit for the random walk Metropolis algorithm out of stationarity ⋮ Collective proposal distributions for nonlinear MCMC samplers: mean-field theory and fast implementation ⋮ Optimal scaling for the transient phase of the random walk Metropolis algorithm: the mean-field limit

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