glabcmcmc : a Python package for ABC-MCMC with local and global moves - Cao, Xuefei; Wang, Shijia; Zhou, Yongdao (Statistical theory and related fields, 2025)
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.