Optimal scaling of the random walk Metropolis algorithm under Lp mean differentiability
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Publication:4684918
DOI10.1017/jpr.2017.61zbMath1401.65007arXiv1604.06664OpenAlexW2344978419MaRDI QIDQ4684918
Gareth O. Roberts, Sylvain Le Corff, Alain Durmus, Eric Moulines
Publication date: 26 September 2018
Published in: Journal of Applied Probability (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1604.06664
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Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics, Optimal scaling of random-walk Metropolis algorithms on general target distributions, Counterexamples for optimal scaling of Metropolis-Hastings chains with rough target densities, Optimal scaling of the random walk Metropolis algorithm under Lp mean differentiability
Cites Work
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- Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets
- Optimal scaling of random walk Metropolis algorithms with discontinuous target densities
- Asymptotic methods in statistical decision theory
- Weak convergence and optimal scaling of random walk Metropolis algorithms
- Singular stochastic differential equations.
- From Metropolis to diffusions: Gibbs states and optimal scaling.
- Optimal scaling for the transient phase of the random walk Metropolis algorithm: the mean-field limit
- Optimal scalings for local Metropolis-Hastings chains on nonproduct targets in high dimensions
- Minimising MCMC variance via diffusion limits, with an application to simulated tempering
- Weak convergence of Metropolis algorithms for non-I.I.D. target distributions
- Optimal acceptance rates for Metropolis algorithms: Moving beyond 0.234
- Optimal scaling of Metropolis algorithms: Heading toward general target distributions
- Optimal scaling of the random walk Metropolis algorithm under Lp mean differentiability
- Equation of State Calculations by Fast Computing Machines
