glabcmcmc : a Python package for ABC-MCMC with local and global moves

We introduce a new Python package glabcmcmc, which implements an approximate Bayesian computation Markov chain Monte Carlo (ABC-MCMC) algorithm that combines global and local proposal strategies to address the limitations of standard ABC-MCMC. The proposed package includes key innovations such as th...

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Published inStatistical theory and related fields Vol. 9; no. 2; pp. 168 - 177
Main Authors Cao, Xuefei, Wang, Shijia, Zhou, Yongdao
Format Journal Article
LanguageEnglish
Published Taylor & Francis Group 03.04.2025
Subjects
Approximate Bayesian Computation
global-local proposal
Markov chain Monte Carlo
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Abstract We introduce a new Python package glabcmcmc, which implements an approximate Bayesian computation Markov chain Monte Carlo (ABC-MCMC) algorithm that combines global and local proposal strategies to address the limitations of standard ABC-MCMC. The proposed package includes key innovations such as the determination of global proposal frequencies, the implementation of a hybrid ABC-MCMC algorithm integrating global and local proposals, and an adaptive version that utilizes normalizing flows and gradient-based computations for enhanced proposal mechanisms. The functionality of the software package is demonstrated through illustrative examples.
AbstractList We introduce a new Python package glabcmcmc, which implements an approximate Bayesian computation Markov chain Monte Carlo (ABC-MCMC) algorithm that combines global and local proposal strategies to address the limitations of standard ABC-MCMC. The proposed package includes key innovations such as the determination of global proposal frequencies, the implementation of a hybrid ABC-MCMC algorithm integrating global and local proposals, and an adaptive version that utilizes normalizing flows and gradient-based computations for enhanced proposal mechanisms. The functionality of the software package is demonstrated through illustrative examples.
Author Zhou, Yongdao
Wang, Shijia
Cao, Xuefei
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Cites_doi 10.1073/pnas.0306899100
10.1093/genetics/162.4.2025
10.1093/oxfordjournals.molbev.a026091
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10.1080/10618600.2013.866048
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10.1080/10618600.2024.2379349
10.1111/2041-210X.12050
10.1080/01621459.2013.864178
10.1186/1471-2105-11-116
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SubjectTerms Approximate Bayesian Computation
global-local proposal
Markov chain Monte Carlo
Title glabcmcmc : a Python package for ABC-MCMC with local and global moves
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