Results 231 to 240 of about 182,801 (272)

Adaptive Rejection Sampling for Gibbs Sampling

Applied Statistics, 1992
Summary: We propose a method for rejection sampling from any univariate log- concave probability density function. The method is adaptive: as sampling proceeds, the rejection envelope and squeezing function are converge to the density function. The technique is intended for situations where evaluation of the density is computationally expensive, in ...
Gilks, W. R., Wild, P.
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Variable Selection Via Gibbs Sampling

Journal of the American Statistical Association, 1993
Abstract A crucial problem in building a multiple regression model is the selection of predictors to include. The main thrust of this article is to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets.
Edward I. George, Robert E. McCulloch
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Capture-Recapture Estimation Via Gibbs Sampling

Biometrika, 1992
Summary: Capture-recapture models are widely used in the estimation of population sizes. Based on data augmentation considerations, we show how Gibbs sampling can be applied to calculate Bayes estimates in this setting. As a result, formulations which were previously avoided because of analytical and numerical intractability can now be easily ...
George, Edward I., Robert, Christian P.
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PharmID:  Pharmacophore Identification Using Gibbs Sampling

Journal of Chemical Information and Modeling, 2006
The binding of a small molecule to a protein is inherently a 3D matching problem. As crystal structures are not available for most drug targets, there is a need to be able to infer from bioassay data the key binding features of small molecules and their disposition in space, the pharmacophore.
Jun, Feng, Ashish, Sanil, S Stanley, Young   +2 more
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18 Gibbs sampling

1993
Publisher Summary This chapter presents an elementary introduction to Gibbs sampling. Gibbs sampling gives a way to approximate posterior distributions in many Bayesian models. It gives a convenient way to approximate the posterior densities of univariate functions of the parameter.
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Gibbs Sampling with Deterministic Dependencies

2011
There is a growing interest in the logical representation of both probabilistic and deterministic dependencies. While Gibbs sampling is a widely-used method for estimating probabilities, it is known to give poor results in the presence of determinism. In this paper, we consider acyclic Horn logic, a small, but significant fragment of first-order logic ...
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Quantum Gibbs Sampling

Markov Chain Monte Carlo algorithms are indispensable in classical thermodynamic simulation, perhaps due to their mathematical simplicity, algorithmic efficiency, and physical origin. In particular, Glauber dynamics is a detailed-balanced continuous-time Markov chain that fixes the Gibbs distribution and also serves as a mathematically succinct model ...
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Convergence Diagnosis for Gibbs Sampling Output

2000
Gibbs sampling is a technique to calculate a complex posterior distribution as steady state measure of a Markov chain. The fundamental problem of inference from Markov chain simulation is that there will always be areas of the target distribution that have not been covered by the finite chain.
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Protein domain hierarchy Gibbs sampling strategies

Statistical Applications in Genetics and Molecular Biology, 2014
AbstractHierarchically-arranged multiple sequence alignment profiles are useful for modeling protein domains that have functionally diverged into evolutionarily-related subgroups. Currently such alignment hierarchies are largely constructed through manual curation, as for the NCBI Conserved Domain Database (CDD).
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