Optimal scaling of MaLa for nonlinear regression.

DOI10.1214/105051604000000369zbMath1048.62062arXivmath/0407132OpenAlexW3099831239MaRDI QIDQ1879917

Mauro Piccioni, Sergio Scarlatti, Laird Breyer

Publication date: 15 September 2004

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

Full work available at URL: https://arxiv.org/abs/math/0407132

zbMATH Keywords

Markov chain Monte Carlopropagation of chaosLangevin diffusionHastings-Metropolis

Mathematics Subject Classification ID

Central limit and other weak theorems (60F05) Bayesian inference (62F15) General nonlinear regression (62J02) Large deviations (60F10) Functional limit theorems; invariance principles (60F17)

Related Items

Asymptotic analysis of the random walk metropolis algorithm on ridged densities ⋮ An adaptive multiple-try Metropolis algorithm ⋮ Optimal scalings for local Metropolis-Hastings chains on nonproduct targets in high dimensions ⋮ Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions ⋮ Optimal scaling of random-walk Metropolis algorithms on general target distributions ⋮ An adaptive version for the Metropolis adjusted Langevin algorithm with a truncated drift ⋮ Diffusion limits of the random walk Metropolis algorithm in high dimensions ⋮ Optimal scaling for the transient phase of Metropolis Hastings algorithms: the longtime behavior ⋮ Weak convergence of Metropolis algorithms for non-I.I.D. target distributions ⋮ Error Bounds and Normalising Constants for Sequential Monte Carlo Samplers in High Dimensions ⋮ On the stability of sequential Monte Carlo methods in high dimensions ⋮ Random walk Metropolis algorithm in high dimension with non-Gaussian target distributions ⋮ Efficiency of delayed-acceptance random walk metropolis algorithms ⋮ Non-stationary phase of the MALA algorithm ⋮ On the efficiency of pseudo-marginal random walk Metropolis algorithms ⋮ Optimal scaling for the transient phase of the random walk Metropolis algorithm: the mean-field limit

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Sanov property, generalized I-projection and a conditional limit theorem
• I-divergence geometry of probability distributions and minimization problems
• Exponential convergence of Langevin distributions and their discrete approximations
• 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.
• Bayesian learning for neural networks
• Methode de laplace: etude variationnelle des fluctuations de diffusions de type
• Optimal Scaling of Discrete Approximations to Langevin Diffusions
• Scaling Limits for the Transient Phase of Local Metropolis–Hastings Algorithms