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A review of approximate Bayesian computation methods via density estimation: Inference for simulator‐models
Author(s) -
Grazian Clara,
Fan Yanan
Publication year - 2019
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1486
Subject(s) - approximate bayesian computation , computer science , computation , bayesian inference , inference , bayesian probability , statistical inference , machine learning , scalability , graphical model , artificial intelligence , frequentist inference , parametric model , parametric statistics , variable order bayesian network , bayesian statistics , data mining , algorithm , mathematics , statistics , database
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