Kinetic Theories for Metropolis Monte Carlo Methods

Fuente: arXiv
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Hauptverfasser: Herty, Michael, Ringhofer, Christian
Format: Preprint
Veröffentlicht: 2024
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author Herty, Michael
Ringhofer, Christian
author_facet Herty, Michael
Ringhofer, Christian
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2405_01232
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Kinetic Theories for Metropolis Monte Carlo Methods
Herty, Michael
Ringhofer, Christian
Optimization and Control
Numerical Analysis
Probability
82C22, 35K55, 65C05
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.
title Kinetic Theories for Metropolis Monte Carlo Methods
topic Optimization and Control
Numerical Analysis
Probability
82C22, 35K55, 65C05
url https://arxiv.org/abs/2405.01232