Adaptive approximate Bayesian computation for complex models [PDF]
, 2013 Approximate Bayesian computation (ABC) is a family of computational techniques in Bayesian statistics. These techniques allow to fi t a model to data without relying on the computation of the model likelihood.
CC Drovandi +13 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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ABrox-A user-friendly Python module for approximate Bayesian computation with a focus on model comparison. [PDF]
PLoS ONE, 2018 We give an overview of the basic principles of approximate Bayesian computation (ABC), a class of stochastic methods that enable flexible and likelihood-free model comparison and parameter estimation.
Ulf Kai Mertens +2 more
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Pre-processing for approximate Bayesian computation in image analysis [PDF]
, 2014 Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration.
Drovandi, Christopher C. +3 more
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Approximate Bayesian Computation for a Class of Time Series Models [PDF]
, 2014 In the following article we consider approximate Bayesian computation (ABC) for certain classes of time series models. In particular, we focus upon scenarios where the likelihoods of the observations and parameter are intractable, by which we mean that ...
Jasra, Ajay
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Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy
Algorithms, 2020 Radioisotope identification (RIID) algorithms for gamma-ray spectroscopy aim to infer what isotopes are present and in what amounts in test items. RIID algorithms either use all energy channels in the analysis region or only energy channels in and near ...
Tom Burr +3 more
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Interpreting scratch assays using pair density dynamics and approximate Bayesian computation [PDF]
Open Biology, 2014 Quantifying the impact of biochemical compounds on collective cell spreading is an essential element of drug design, with various applications including developing treatments for chronic wounds and cancer.
Stuart T. Johnston +4 more
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Approximate Bayesian computation methods [PDF]
Statistics and Computing, 2012 Occasionally, Statistics and Computing is publishing Special Issues on topics of potential interests. The most recent published Special Issues were concerned with “Adaptive Methods in Bayesian Computation”, Guest Editor Paul Fearnhead, Volume 18 Issue 4 (2008), “Regularisation Methods in Classification and Regression”, Guest Editor Gerhard Tutz, Volume
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Constructing Summary Statistics for Approximate Bayesian Computation: Semi-Automatic Approximate Bayesian Computation [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2012 Summary Many modern statistical applications involve inference for complex stochastic models, where it is easy to simulate from the models, but impossible to calculate likelihoods. Approximate Bayesian computation (ABC) is a method of inference for such models.
Paul Fearnhead, Dennis Prangle
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Correcting Approximate Bayesian Computation [PDF]
Trends in Ecology & Evolution, 2010 In their review of approximate Bayesian computation (ABC), Csillery et al. [pg. 411, 1] stated that my [2] “main” objections to ABC are that inference is limited to a finite set of models, and that these models are generally complex, although they failed to state the reasons for my objections. Csillery et al.
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