A review of approximate Bayesian computation methods via density estimation: Inference for simulator‐models. (29th October 2019)

• Record Type: Journal Article
• Authors: Grazian, Clara; Fan, Yanan
• Is Part Of: Wiley interdisciplinary reviews. Volume 12:Number 4(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-10-29
• Subjects: approximate Bayesian computation -- neural density estimation -- synthetic likelihood; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1486
• Languages: English
• ISSNs: 1939-5108

Abstract

Abstract: This paper provides a review of approximate Bayesian computation (ABC) methods for carrying out Bayesian posterior inference, through the lens of density estimation. We describe several recent algorithms and make connection with traditional approaches. We show advantages and limitations of models based on parametric approaches and we then draw attention to developments in machine learning, which we believe have the potential to make ABC scalable to higher dimensions and may be the future direction for research in this area. This article is categorized under: Algorithms and Computational Methods < Algorithms Statistical and Graphical Methods of Data Analysis < Bayesian Methods and Theory Statistical Models < Simulation Models Abstract : Posterior inference using simulator models (the Ricker model).