Results 21 to 30 of about 135,519 (174)
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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Approximate Bayesian Computation for Smoothing [PDF]
Stochastic Analysis and Applications, 2014 We consider a method for approximate inference in hidden Markov models (HMMs). The method circumvents the need to evaluate conditional densities of observations given the hidden states. It may be considered an instance of Approximate Bayesian Computation (ABC) and it involves the introduction of auxiliary variables valued in the same space as the ...
Martin, JS +5 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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Model Selection in Historical Research Using Approximate Bayesian Computation. [PDF]
PLoS ONE, 2016 FORMAL MODELS AND HISTORY:Computational models are increasingly being used to study historical dynamics. This new trend, which could be named Model-Based History, makes use of recently published datasets and innovative quantitative methods to improve our
Xavier Rubio-Campillo
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Epidemics, 2023 Assessing the factors responsible for differences in outbreak severity for the same pathogen is a challenging task, since outbreak data are often incomplete and may vary in type across outbreaks (e.g., daily case counts, serology, cases per household ...
Alexander D. Meyer +5 more
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Frontiers in Microbiology, 2018 To estimate and predict the transmission dynamics of respiratory viruses, the estimation of the basic reproduction number, R0, is essential. Recently, approximate Bayesian computation methods have been used as likelihood free methods to estimate ...
Heidi L. Tessmer +3 more
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Approximation Bayesian computation [PDF]
OA Genetics, 2013 Approximation Bayesian computation [ABC] is an analysis approach that has arisen in response to the recent trend to collect data that is of a magnitude far higher than has been historically the case. This has led to many existing methods become intractable because of difficulties in calculating the likelihood function.
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Approximate Bayesian Computation via Classification
, 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.
Wang, Yuexi +2 more
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Adaptive approximate Bayesian computation [PDF]
Biometrika, 2009 Sequential techniques can enhance the efficiency of the approximate Bayesian computation algorithm, as in Sisson et al.'s (2007) partial rejection control version. While this method is based upon the theoretical works of Del Moral et al. (2006), the application to approximate Bayesian computation results in a bias in the approximation to the posterior.
Beaumont, M. A. +3 more
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