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Approximate Bayesian Computation [PDF]
Annual Review of Statistics and Its Application, 2019 Many of the statistical models that could provide an accurate, interesting, and testable explanation for the structure of a data set turn out to have intractable likelihood functions. The method of approximate Bayesian computation (ABC) has become a popular approach for tackling such models.
Osvaldo A. Martin +2 more
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Hierarchical Approximate Bayesian Computation [PDF]
Psychometrika, 2014 Approximate Bayesian computation (ABC) is a powerful technique for estimating the posterior distribution of a model’s parameters. It is especially important when the model to be fit has no explicit likelihood function, which happens for computational (or simulation-based) models such as those that are popular in cognitive neuroscience and other areas ...
Turner, Brandon M., Van Zandt, Trisha
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Approximate Bayesian computational methods [PDF]
Statistics and Computing, 2011 7 ...
Jean-Michel Marin +3 more
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An overview on Approximate Bayesian computation [PDF]
ESAIM: Proceedings, 2014 Approximate Bayesian computation techniques, also called likelihood-free methods, are one of the most satisfactory approach to intractable likelihood problems.
Baragatti, Meïli, Pudlo, Pierre
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Bayesian parameter inference and model selection by population annealing in systems biology. [PDF]
PLoS ONE, 2014 Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection.
Yohei Murakami
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Software for Bayesian Statistics
Journal of Statistical Software, 2021 In this summary we introduce the papers published in the special issue on Bayesian statistics. This special issue comprises 20 papers on Bayesian statistics and Bayesian inference on different topics such as general packages for hierarchical linear model
Michela Cameletti, Virgilio Gómez-Rubio
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Approximate Bayesian Computation via Classification
J. Mach. Learn. Res., 2021 Approximate Bayesian Computation (ABC) enables statistical inference in simulator-based models whose likelihoods are difficult to calculate but easy to simulate from. ABC constructs a kernel-type approximation to the posterior distribution through an accept/reject mechanism which compares summary statistics of real and simulated data.
Yuexi Wang +2 more
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Demographic inference through approximate-Bayesian-computation skyline plots [PDF]
PeerJ, 2017 The skyline plot is a graphical representation of historical effective population sizes as a function of time. Past population sizes for these plots are estimated from genetic data, without a priori assumptions on the mathematical function defining the ...
Miguel Navascués +2 more
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Curve Registration of Functional Data for Approximate Bayesian Computation
Stats, 2021 Approximate Bayesian computation is a likelihood-free inference method which relies on comparing model realisations to observed data with informative distance measures.
Anthony Ebert +3 more
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ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
Journal of Statistical Software, 2021 ABCpy is a highly modular scientific library for approximate Bayesian computation (ABC) written in Python. The main contribution of this paper is to document a software engineering effort that enables domain scientists to easily apply ABC to their ...
Ritabrata Dutta +7 more
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