Approximate Bayesian computational methods

2011/01/05 by Jean‐Michel Marin, Jean-Michel Marin, Pierre Pudlo +7 · 61 citations
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Gaussian Processes and Bayesian Inference #Markov Chains and Monte Carlo Methods

paper · doi:10.1007/s11222-011-9288-2

openalex publication_date 2011/10/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

Also known as likelihood-free methods, approximate Bayesian computational (ABC) methods have appeared in the past ten years as the most satisfactory approach to untractable likelihood problems, first in genetics then in a broader spectrum of applications. However, these methods suffer to some degree from calibration difficulties that make them rather volatile in their implementation and thus render them suspicious to the users of more traditional Monte Carlo methods. In this survey, we study the various improvements and extensions made to the original ABC algorithm over the recent years.

Citations

• Lack of confidence in ABC model choice

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