Gibbs sampling, adaptive rejection sampling and robustness to prior specification for a mixed linear model [PDF]
Genetics Selection Evolution, 1997 Markov chain Monte-Carlo methods are increasingly being applied to make inferences about the marginal posterior distributions of parameters in quantitative genetic models.
Thompson R, Firat MZ, Theobald CM
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Topical Text Network Construction Method Based on Gibbs Sampling Results [PDF]
Jisuanji gongcheng, 2017 Mining the probability distribution of topic words in document collection can make a summary understanding of the document content.Further exploring the connection relationship between words in a given topic not only riches the meaning of topic words,but
ZHANG Zhiyuan,YANG Hongjing,ZHAO Yue
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Productioon uncertainties modelling by Bayesian inference using Gibbs sampling
South African Journal of Industrial Engineering, 2015 Analysis by modelling production throughput is an efficient way to provide information for production decision-making. Observation and investigation based on a real-life tile production line revealed that the five main uncertain variables are demand rate,
Azizi, Amir +3 more
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Bayesian nonparametric estimators derived from conditional Gibbs structures [PDF]
, 2008 We consider discrete nonparametric priors which induce Gibbs-type exchangeable random partitions and investigate their posterior behavior in detail.
PRUENSTER, IGOR +5 more
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Posterior analysis of stochastic frontier models using Gibbs sampling [PDF]
, 1992 In this paper we describe the use of Gibbs sampling methods for making posterior inferences in stochastic frontier models with composed error. We show how the Gibbs sampler can greatly reduce the computational difficulties involved in analyzing such ...
Osiewalski, Jacek +2 more
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Representation of complex probabilities and complex Gibbs sampling
EPJ Web of Conferences, 2018 Complex weights appear in Physics which are beyond a straightforward importance sampling treatment, as required in Monte Carlo calculations. This is the wellknown sign problem.
Salcedo Lorenzo Luis
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Gibbs sampling will fail in outlier problems with strong masking [PDF]
, 1995 This paper discusses the convergence of the Gibbs sampling algorithm when it is applied to the problem of outlier detection in regression models. Given any vector of initial conditions, theoretically, the algorithm converges to the true posterior ...
Justel, Ana, Peña, Daniel
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Fast algorithms for band-limited extrapolation by over sampling and Fourier series
EURASIP Journal on Advances in Signal Processing, 2023 In this paper, fast algorithms for the extrapolation of band-limited signals are presented by the sampling theorem and Fourier series in the case of over sampling. Assume the band-limited signal is known in a finite interval. We update the signal outside
Weidong Chen
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PhyloGibbs: a Gibbs sampling motif finder that incorporates phylogeny.
PLoS Computational Biology, 2005 A central problem in the bioinformatics of gene regulation is to find the binding sites for regulatory proteins. One of the most promising approaches toward identifying these short and fuzzy sequence patterns is the comparative analysis of orthologous ...
Rahul Siddharthan +2 more
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Sampling from Dirichlet process mixture models with unknown concentration parameter: mixing issues in large data implementations [PDF]
, 2014 We consider the question of Markov chain Monte Carlo sampling from a general stick-breaking Dirichlet process mixture model, with concentration parameter (Formula presented.).
Silvia Liverani +5 more
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