Results 11 to 20 of about 149,229 (279)
, 2010 We consider various versions of adaptive Gibbs and Metropolis- within-Gibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on the fly during a run, by learning as they go in an attempt to optimise
Rosenthal, Jeffrey S. (Jeffrey Seth) +1 more
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Facilitating the Gibbs Sampler: The Gibbs Stopper and the Griddy-Gibbs Sampler
Journal of the American Statistical Association, 1992 The article briefly reviews the history, literature, and form of the Gibbs sampler. An importance sampling device is proposed for converting the output of the Gibbs sampler to a sample from the exact posterior. This Gibbs stopper technique is also useful
Ritter, Christian, Tanner, Martin A.
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On a multivariate implementation of the Gibbs sampler [PDF]
Genetics Selection Evolution, 1996 Il est bien etabli que, lorsque les parametres d'un modele sont correles, l'estimation de ces parametres par echantillonnage de Gibbs converge lentement lorsque les composantes du modele sont traitees separement. Mais, si l'echantillonnage de Gibbs est conduit en fixant des valeurs pour les parametres correles et en echantillonnant dans les ...
García-Cortés LA, Sorensen D
doaj +4 more sources
A partially collapsed Gibbs sampler for Bayesian quantile regression [PDF]
, 2009 We introduce a set of new Gibbs sampler for Bayesian analysis of quantile re-gression model. The new algorithm, which partially collapsing an ordinary Gibbs sampler, is called Partially Collapsed Gibbs (PCG) sampler.
Yu, K, Reed, C, Keming Yu, Craig Reed
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The Gibbs Centroid Sampler [PDF]
Nucleic Acids Research, 2007 The Gibbs Centroid Sampler is a software package designed for locating conserved elements in biopolymer sequences. The Gibbs Centroid Sampler reports a centroid alignment, i.e. an alignment that has the minimum total distance to the set of samples chosen from the a posteriori probability distribution of transcription factor binding-site alignments.
William A. Thompson +4 more
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Adapting the Gibbs sampler [PDF]
In the present thesis, we close a methodological gap of optimising the basic Markov Chain Monte Carlo algorithms. Similarly to the straightforward and computationally efficient optimisation criteria for the Metropolis algorithm acceptance rate (and ...
Chimisov, Cyril
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Blind deconvolution of sparse pulse sequences under a minimum distance constraint: a partially collapsed Gibbs sampler method [PDF]
, 2012 For blind deconvolution of an unknown sparse sequence convolved with an unknown pulse, a powerful Bayesian method employs the Gibbs sampler in combination with a Bernoulli–Gaussian prior modeling sparsity.
Kail, Georg +3 more
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CoRR, 2018 Gibbs sampling, as a model learning method, is known to produce the most accurate results available in a variety of domains, and is a de facto standard in these domains. Yet, it is also well known that Gibbs random walks usually have bottlenecks, sometimes termed "local maxima", and thus samplers often return suboptimal solutions.
Mark Kozdoba, Shie Mannor
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The Recycling Gibbs sampler for efficient learning [PDF]
Digital Signal Processing, 2018 Monte Carlo methods are essential tools for Bayesian inference. Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, machine learning, and statistics, employed to draw samples from complicated high-dimensional posterior distributions.
Luca Martino +2 more
openaire +7 more sources