Kinetic Theories for Metropolis Monte Carlo Methods
Optimization and Control
2024-05-03 v1 Numerical Analysis
Numerical Analysis
Probability
Abstract
We consider generalizations of the classical inverse problem to Bayesien type estimators, where the result is not one optimal parameter but an optimal probability distribution in parameter space. The practical computational tool to compute these distributions is the Metropolis Monte Carlo algorithm. We derive kinetic theories for the Metropolis Monte Carlo method in different scaling regimes. The derived equations yield a different point of view on the classical algorithm. It further inspired modifications to exploit the difference scalings shown on an simulation example of the Lorenz system.
Cite
@article{arxiv.2405.01232,
title = {Kinetic Theories for Metropolis Monte Carlo Methods},
author = {Michael Herty and Christian Ringhofer},
journal= {arXiv preprint arXiv:2405.01232},
year = {2024}
}