Results 61 to 70 of about 196,289 (193)
Parallel Markov chain Monte Carlo simulations [PDF]
The Journal of Chemical Physics, 2007 With strict detailed balance, parallel Monte Carlo simulation through domain decomposition cannot be validated with conventional Markov chain theory, which describes an intrinsically serial stochastic process. In this work, the parallel version of Markov chain theory and its role in accelerating Monte Carlo simulations via cluster computing is explored.
Ren, Ruichao, Orkoulas, G.
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Genetics Selection Evolution, 2019 Background Hamiltonian Monte Carlo is one of the algorithms of the Markov chain Monte Carlo method that uses Hamiltonian dynamics to propose samples that follow a target distribution.
Motohide Nishio, Aisaku Arakawa
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Perceptual multistability as Markov Chain Monte Carlo inference [PDF]
, 2017 While many perceptual and cognitive phenomena are well described in terms of Bayesian inference, the necessary computations are intractable at the scale of real-world tasks, and it remains unclear how the human mind approximates Bayesian computations ...
Gershman, Samuel J. +2 more
core
Convergence Diagnostics for Markov Chain Monte Carlo [PDF]
Annual Review of Statistics and Its Application, 2020 Markov chain Monte Carlo (MCMC) is one of the most useful approaches to scientific computing because of its flexible construction, ease of use, and generality. Indeed, MCMC is indispensable for performing Bayesian analysis. Two critical questions that MCMC practitioners need to address are where to start and when to stop the simulation.
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MCMC-ODPR: Primer design optimization using Markov Chain Monte Carlo sampling
BMC Bioinformatics, 2012 Background Next generation sequencing technologies often require numerous primer designs that require good target coverage that can be financially costly.
Kitchen James L +3 more
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Computation of identity by descent probabilities conditional on DNA markers via a Monte Carlo Markov Chain method [PDF]
, 2000 Miguel Pérez‐Enciso +2 more
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Hastings-Metropolis algorithm on Markov chains for small-probability estimation***
ESAIM: Proceedings and Surveys, 2015 Shielding studies in neutron transport, with Monte Carlo codes, yield challenging problems of small-probability estimation. The particularity of these studies is that the small probability to estimate is formulated in terms of the ...
Bachoc Francois +2 more
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Ideal Observer Computation by Use of Markov-Chain Monte Carlo With Generative Adversarial Networks. [PDF]
IEEE Trans Med Imaging, 2023 Zhou W, Villa U, Anastasio MA.
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, 2019 We propose a new computationally efficient sampling scheme for Bayesian inference involving high dimensional probability distributions. Our method maps the original parameter space into a low-dimensional latent space, explores the latent space to generate samples, and maps these samples back to the original space for inference.
Shahbaba, Babak +3 more
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Seriation in paleontological data using markov chain Monte Carlo methods.
PLoS Computational Biology, 2006 Given a collection of fossil sites with data about the taxa that occur in each site, the task in biochronology is to find good estimates for the ages or ordering of sites. We describe a full probabilistic model for fossil data.
Kai Puolamäki +2 more
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