Results 61 to 70 of about 135,519 (174)
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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Strategies for improving approximate Bayesian computation tests for synchronous diversification
BMC Evolutionary Biology, 2017 Background Estimating the variability in isolation times across co-distributed taxon pairs that may have experienced the same allopatric isolating mechanism is a core goal of comparative phylogeography.
Isaac Overcast +2 more
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Scalable Inference for Markov Processes with Intractable Likelihoods
, 2014 Bayesian inference for Markov processes has become increasingly relevant in recent years. Problems of this type often have intractable likelihoods and prior knowledge about model rate parameters is often poor.
Gillespie, Colin S. +2 more
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Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models [PDF]
, 2018 A computationally simple approach to inference in state space models is proposed, using approximate Bayesian computation (ABC). ABC avoids evaluation of an intractable likelihood by matching summary statistics for the observed data with statistics ...
Frazier, David T. +4 more
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Copula Approximate Bayesian Computation Using Distribution Random Forests
StatsOngoing modern computational advancements continue to make it easier to collect increasingly large and complex datasets, which can often only be realistically analyzed using models defined by intractable likelihood functions.
George Karabatsos
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Approximate Bayesian Computation with composite score functions
, 2015 Both Approximate Bayesian Computation (ABC) and composite likelihood methods are useful for Bayesian and frequentist inference, respectively, when the likelihood function is intractable.
Ruli, Erlis +2 more
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Gibbs flow for approximate transport with applications to Bayesian computation [PDF]
, 2020 Let $\pi_{0}$ and $\pi_{1}$ be two distributions on the Borel space $(\mathbb{R}^{d},\mathcal{B}(\mathbb{R}^{d}))$. Any measurable function $T:\mathbb{R}^{d}\rightarrow\mathbb{R}^{d}$ such that $Y=T(X)\sim\pi_{1}$ if $X\sim\pi_{0}$ is called a transport ...
Doucet, Arnaud +2 more
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Approximate Methods for Bayesian Computation
Annual Review of Statistics and Its Application, 2023 Rich data generating mechanisms are ubiquitous in this age of information and require complex statistical models to draw meaningful inference. While Bayesian analysis has seen enormous development in the last 30 years, benefitting from the impetus given by the successful application of Markov chain Monte Carlo (MCMC) sampling, the combination of big ...
Craiu, Radu V., Levi, Evgeny
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GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation [PDF]
, 2014 Scientists often express their understanding of the world through a computationally demanding simulation program. Analyzing the posterior distribution of the parameters given observations (the inverse problem) can be extremely challenging.
Meeds, Edward, Welling, Max
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Melanoma Cell Colony Expansion Parameters Revealed by Approximate Bayesian Computation.
PLoS Computational Biology, 2015 In vitro studies and mathematical models are now being widely used to study the underlying mechanisms driving the expansion of cell colonies. This can improve our understanding of cancer formation and progression.
Brenda N Vo +3 more
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