3 papers
stat.CO2026
A multifidelity approximate Bayesian computation with pre-filtering
Xuefei Cao, Shijia Wang, Yongdao Zhou
Approximate Bayesian Computation (ABC) methods often require extensive simulations, resulting in high computational costs. This paper focuses on multifidelity simulation models and…
stat.CO2024
An adaptive approximate Bayesian computation MCMC with Global-Local proposals
Xuefei Cao, Shijia Wang, Yongdao Zhou
In this paper, we address the challenge of Markov Chain Monte Carlo (MCMC) algorithms within the approximate Bayesian Computation (ABC) framework, which often get trapped in local…
stat.CO2024
Using early rejection Markov chain Monte Carlo and Gaussian processes to accelerate ABC methods
Xuefei Cao, Shijia Wang, Yongdao Zhou
Approximate Bayesian computation (ABC) is a class of Bayesian inference algorithms that targets for problems with intractable or {unavailable} likelihood function. It uses syntheti…