Comparison of MCMC Adaptation Schemes: A Preliminary Empirical Study

Bowen Liu, Edna Milgo, Nixon Ronoh, Manderick Bernard

Onderzoeksoutput: Conference paper

Samenvatting

Adaptive Markov Chain Monte Carlo (MCMC) adapts the covariance of the proposal distribution to improve the efficiency of Metropolis Hastings (MH). Adaptive Metropolis (AM) is the prime example. Some stochastic optimisation techniques adapt the covariance of the search distribution. Some examples are Gaussian Adaptation (GaA) and (1+1)-Covariance Matrix Adaptation Evolution Strategy (CMAES) that can be turned into MCMC samplers in a straightforward way. However, the adaptation rational used by these samplers differ. AM estimates the covariance of the target distribution based on the generated samples. GaA adapts the covariance such that the entropy of the proposal is increased/decreased when the candidate sample is accepted/rejected while adaptation in CMAES increases the likelihood of generating better search points. We compare the performance of AM, GaA and (1+1)-CMAES samplers on a test suite of target distributions to understand the effectiveness of the adaptation mechanism used.

Originele taal-2English
TitelGECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion
UitgeverijACM Digital Library
Pagina's303–306
Aantal pagina's4
ISBN van elektronische versie9798400701207
DOI's
StatusPublished - 15 jul 2023
EvenementGECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation -
Duur: 1 jul 20231 jul 2023

Publicatie series

NaamGECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion

Conference

ConferenceGECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation
Periode1/07/231/07/23

Bibliografische nota

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© 2023 Copyright held by the owner/author(s).

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