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Efficient learning in Approximate Bayesian Computation [PDF]
, 2011 Efficient learning in Approximate Bayesian ...
Mohammed Sedki, Pierre Pudlo
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New insights into Approximate Bayesian Computation
Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 2015 Approximate Bayesian Computation (ABC for short) is a family of computational techniques which offer an almost automated solution in situations where evaluation of the posterior likelihood is computationally prohibitive, or whenever suitable likelihoods are not available.
Biau, Gérard +2 more
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Automating approximate Bayesian computation by local linear regression
BMC Genetics, 2009 Background In several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular.
Thornton Kevin R
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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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Information Geometry for Approximate Bayesian Computation [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2020 The goal of this paper is to explore the basic Approximate Bayesian Computation (ABC) algorithm via the lens of information theory. ABC is a widely used algorithm in cases where the likelihood of the data is hard to work with or intractable, but one can simulate from it.
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Learning Functions and Approximate Bayesian Computation Design: ABCD
Entropy, 2014 A general approach to Bayesian learning revisits some classical results, which study which functionals on a prior distribution are expected to increase, in a preposterior sense.
Markus Hainy +2 more
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Asymptotic properties of approximate Bayesian computation [PDF]
Biometrika, 2018 Approximate Bayesian computation (ABC) is becoming an accepted tool for statistical analysis in models with intractable likelihoods. With the initial focus being primarily on the practical import of ABC, exploration of its formal statistical properties has begun to attract more attention.
Frazier, David T. +3 more
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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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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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Approximate Bayesian Computation in Population Genetics [PDF]
Genetics, 2002 AbstractWe propose a new method for approximate Bayesian statistical inference on the basis of summary statistics. The method is suited to complex problems that arise in population genetics, extending ideas developed in this setting by earlier authors.
Mark A, Beaumont +2 more
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