Solving Bayesian inverse problems via Fisher adaptive Metropolis adjusted Langevin algorithm [PDF]
The preconditioned Metropolis adjusted Langevin algorithm (MALA) is a widely used method in statistical applications, where the choice of the preconditioning matrix plays a critical role. Recently, Titsias \cite{Titsias2024} demonstrated that the inverse Fisher information matrix is the optimal preconditioner by minimizing the expected squared jump ...
Lili Wang, Guanghui Zheng
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High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm [PDF]
In this work, we propose a first-order sampling method called the Metropolis-adjusted Preconditioned Langevin Algorithm for approximate sampling from a target distribution whose support is a proper convex subset of $\mathbb{R}^{d}$. Our proposed method is the result of applying a Metropolis-Hastings filter to the Markov chain formed by a single step of
Vishwak Srinivasan +2 more
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Exact algorithms for simulation of diffusions with discontinuous drift and robust Curvature Metropolis-adjusted Langevin algorithms [PDF]
, 2015 In our work we propose new Exact Algorithms for simulation of diffusions with discontinuous drift and new methodology for simulating Brownian motion jointly with its local time. In the second part of the thesis we introduce Metropolis-adjusted Langevin algorithm which uses local geometry and we prove geometric ergodicity in case of benchmark ...
Katarzyna B. Taylor
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Introduction to “Quantitative bounds of convergence for geometrically ergodic Markov Chain in the Wasserstein distance with application to the Metropolis adjusted Langevin algorithm” by A. Durmus, É. Moulines [PDF]
Statistics and Computing, 2014 Heikki Haario
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Locally Scaled and Stochastic Volatility Metropolis– Hastings Algorithms
Algorithms, 2021 Markov chain Monte Carlo (MCMC) techniques are usually used to infer model parameters when closed-form inference is not feasible, with one of the simplest MCMC methods being the random walk Metropolis–Hastings (MH) algorithm.
Wilson Tsakane Mongwe +2 more
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Bayesian inference based on algorithms: MH, HMC, MALA and Lip-MALA for prestack seismic inversion [PDF]
Nonlinear Processes in GeophysicsSeismic inversion for estimating elastic properties is a key technique for reservoir characterization after drilling. The choice of inversion method strongly influences the accuracy, efficiency, and reliability of results.
R. Perez-Roa +4 more
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Information-geometric Markov Chain Monte Carlo methods using Diffusions [PDF]
, 2014 Recent work incorporating geometric ideas in Markov chain Monte Carlo is reviewed in order to highlight these advances and their possible application in a range of domains beyond Statistics. A full exposition of Markov chains and their use in Monte Carlo
Girolami, Mark, Livingstone, Samuel
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Non-reversible Metropolis-Hastings [PDF]
, 2015 The classical Metropolis-Hastings (MH) algorithm can be extended to generate non-reversible Markov chains. This is achieved by means of a modification of the acceptance probability, using the notion of vorticity matrix.
Bierkens, Joris
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Error analysis of the transport properties of Metropolized schemes***
ESAIM: Proceedings and Surveys, 2015 We consider in this work the numerical computation of transport coefficients for Brownian dynamics. We investigate the discretization error arising when simulating the dynamics with the Smart MC algorithm (also known as Metropolis ...
Fathi Max +2 more
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MCMC inference for Markov Jump Processes via the Linear Noise Approximation [PDF]
, 2012 Bayesian analysis for Markov jump processes is a non-trivial and challenging problem. Although exact inference is theoretically possible, it is computationally demanding thus its applicability is limited to a small class of problems.
Girolami, Mark A. +1 more
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