Results 121 to 130 of about 2,849 (140)

A neural network assisted Metropolis adjusted Langevin algorithm

Monte Carlo Methods and Applications, 2020
Abstract In this paper, we derive a Markov chain Monte Carlo (MCMC) algorithm supported by a neural network. In particular, we use the neural network to substitute derivative calculations made during a Metropolis adjusted Langevin algorithm (MALA) step with inexpensive neural network evaluations.
Christian Müller, Holger Diedam, Thomas Mrziglod, Andreas Schuppert   +3 more
exaly   +5 more sources

Quantitative bounds of convergence for geometrically ergodic Markov chain in the Wasserstein distance with application to the Metropolis Adjusted Langevin Algorithm

Statistics and Computing, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alain Durmus, Éric Moulines
exaly   +4 more sources

Metropolis-Adjusted Langevin Algorithm with SPSA-Approximated Gradients

2024 American Control Conference (ACC)
Shiqing Sun, James C. Spall
exaly   +4 more sources

On geometric convergence for the Metropolis-adjusted Langevin algorithm under simple conditions

Biometrika, 2023
SummaryWhile the Metropolis-adjusted Langevin algorithm is a popular and widely used Markov chain Monte Carlo method, very few papers derive conditions that ensure its convergence. In particular, to the authors’ knowledge, assumptions that are both easy to verify and guarantee geometric convergence, are still missing.
Alain Oliviero-Durmus, Éric Moulines
openalex   +3 more sources

Non-stationary Phase of the Metropolis-adjusted Langevin Algorithm with Annealed Proposals

Methodology and Computing in Applied Probability
Mylène Bédard
exaly   +4 more sources

Sequential Parallel Metropolis-Adjusted Langevin Algorithm on Matrix Lie Groups

Lecture Notes in Computer Science
Enzo Lopez, Karim Dahia, Nicolas Merlinge, Bénédicte Winter-Bonnet, Alain Maschiella, Christian Musso   +5 more
exaly   +4 more sources

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Parameter estimation of chirp signals using the metropolis-adjusted-langevin's algorithm

Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004., 2005
This paper addresses the problem of parameter estimation of chirp signals in additive Gaussian white noise. A new Markov chain Monte Carlo (MCMC) method called the metropolis-adjusted-Langevin's (MAL) algorithm is employed to solve this problem, which is faster to converge than the random walk metropolis-hastings (MH) algorithm.
null Yan Lin, null Xintan Wang, null Yingning Peng   +2 more
openaire   +1 more source

Optimal scaling of Metropolis adjusted Langevin algorithms for nonlinear regression

2004
We address the problem of simulating efficiently from the posterior distribution over the parameters of a particular class of nonlinear regression models using a Langevin–Metropolis sampler. It is shown that as the number of parameters increases, the proposal variance must scale in a precise way in order to converge to a diffusion.
BREYER L. A., PICCIONI, MAURO, SCARLATTI S.   +2 more
openaire   +1 more source

Hamiltonian-Assisted Metropolis Sampling

Journal of the American Statistical Association, 2023
Zhiqiang Tan
exaly  

A Shrinkage-Thresholding Metropolis Adjusted Langevin Algorithm for Bayesian Variable Selection

IEEE Journal on Selected Topics in Signal Processing, 2016
Gersende Fort
exaly