Particle Metropolis-adjusted Langevin algorithms [PDF]
Biometrika, 2016 Accepted to Biometrika. Main text: 22 pages and 3 figures.
Christopher Nemeth +2 more
exaly +7 more sources
Gaussian Approximations of SDES in Metropolis-Adjusted Langevin Algorithms [PDF]
2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021 Markov chain Monte Carlo (MCMC) methods are a cornerstone of Bayesian inference and stochastic simulation. The Metropolis-adjusted Langevin algorithm (MALA) is an MCMC method that relies on the simulation of a stochastic differential equation (SDE) whose stationary distribution is the desired target density using the Euler-Maruyama algorithm and ...
Simo Särkkä +2 more
exaly +6 more sources
Langevin diffusions and the Metropolis-adjusted Langevin algorithm [PDF]
Statistics & Probability Letters, 2014 We provide a clarification of the description of Langevin diffusions on Riemannian manifolds and of the measure underlying the invariant density. As a result we propose a new position-dependent Metropolis-adjusted Langevin algorithm (MALA) based upon a Langevin diffusion in $\mathbb{R}^d$ which has the required invariant density with respect to ...
Tatiana Xifara +4 more
exaly +7 more sources
Efficiently handling constraints with Metropolis-adjusted Langevin algorithm [PDF]
, 2023 In this study, we investigate the performance of the Metropolis-adjusted Langevin algorithm in a setting with constraints on the support of the target distribution. We provide a rigorous analysis of the resulting Markov chain, establishing its convergence and deriving an upper bound for its mixing time.
Jinyuan Chang +2 more
openalex +3 more sources
autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm [PDF]
, 2023 Selecting the step size for the Metropolis-adjusted Langevin algorithm (MALA) is necessary in order to obtain satisfactory performance. However, finding an adequate step size for an arbitrary target distribution can be a difficult task and even the best step size can perform poorly in specific regions of the space when the target distribution is ...
Miguel Biron-Lattes +4 more
openalex +3 more sources
Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm [PDF]
, 2020 41 ...
Sinho Chewi +5 more
+5 more sources
When does Metropolized Hamiltonian Monte Carlo provably outperform Metropolis-adjusted Langevin algorithm? [PDF]
, 2023 We analyze the mixing time of Metropolized Hamiltonian Monte Carlo (HMC) with the leapfrog integrator to sample from a distribution on $\mathbb{R}^d$ whose log-density is smooth, has Lipschitz Hessian in Frobenius norm and satisfies isoperimetry. We bound the gradient complexity to reach $ε$ error in total variation distance from a warm start by ...
Yuansi Chen +2 more
openalex +3 more sources
Hessian corrections to the Metropolis Adjusted Langevin Algorithm [PDF]
, 2015 7 pages, 3 ...
Thomas House
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Bayesian inference of local government audit outcomes. [PDF]
PLoS ONE, 2021 The scandals in publicly listed companies have highlighted the large losses that can result from financial statement fraud and weak corporate governance. Machine learning techniques have been applied to automatically detect financial statement fraud with
Wilson Tsakane Mongwe +2 more
doaj +2 more sources
A Shrinkage-Thresholding Metropolis Adjusted Langevin Algorithm for Bayesian Variable Selection [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2015 This paper introduces a new Markov Chain Monte Carlo method for Bayesian variable selection in high dimensional settings. The algorithm is a Hastings-Metropolis sampler with a proposal mechanism which combines a Metropolis Adjusted Langevin (MALA) step to propose local moves associated with a shrinkage-thresholding step allowing to propose new models ...
Amandine Schreck +3 more
openalex +5 more sources