Work

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

Year: 2025

Type: software-paper

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.

Source: Statistical Theory and Related Fields

DOI: 10.1080/24754269.2025.2495505

Authors: Xuefei Cao, Shijia Wang, Yongdao Zhou

Institutions: Nankai University, ShanghaiTech University

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, ShanghaiTech University, Fundamental Research Funds for the Central Universities

Awards: G2931448909, G6152136666

OpenAlex ID: W4410085827

Cites: 12

Cited by: 2

Related to: 10