Results 31 to 40 of about 135,519 (174)
Efficient learning in Approximate Bayesian Computation [PDF]
, 2011 Efficient learning in Approximate Bayesian ...
Mohammed Sedki, Pierre Pudlo
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Approximate Bayesian computation with functional statistics [PDF]
Statistical Applications in Genetics and Molecular Biology, 2013 Functional statistics are commonly used to characterize spatial patterns in general and spatial genetic structures in population genetics in particular. Such functional statistics also enable the estimation of parameters of spatially explicit (and genetic) models.
Soubeyrand, Samuel +3 more
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Computation of Kullback–Leibler Divergence in Bayesian Networks
Entropy, 2021 Kullback–Leibler divergence KL(p,q) is the standard measure of error when we have a true probability distribution p which is approximate with probability distribution q.
Serafín Moral +2 more
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Exact Inference with Approximate Computation for Differentially Private Data via Perturbations
The Journal of Privacy and Confidentiality, 2022 This paper discusses how two classes of approximate computation algorithms can be adapted, in a modular fashion, to achieve exact statistical inference from differentially private data products.
Ruobin Gong
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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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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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On the Asymptotic Efficiency of Approximate Bayesian Computation Estimators [PDF]
, 2017 Many statistical applications involve models for which it is difficult to evaluate the likelihood, but from which it is relatively easy to sample. Approximate Bayesian computation is a likelihood-free method for implementing Bayesian inference in such ...
Fearnhead, Paul, Li, Wentao
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Approximate Bayesian Computation with Path Signatures
, 2021 42 pages, 8 ...
Dyer, J, Cannon, P, Schmon, SM
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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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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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