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.

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To cite this article: Xuefei Cao, Shijia Wang & Yongdao Zhou (2025) glabcmcmc: a Python package for ABC-MCMC with local and global moves, Statistical Theory and Related Fields, 9:2, 168-177, DOI: 10.1080/24754269.2025.2495505

To link to this article: https://doi.org/10.1080/24754269.2025.2495505