Results 21 to 30 of about 182,801 (272)
Gibbs-Slice Sampling Algorithm for Estimating the Four-Parameter Logistic Model
Frontiers in Psychology, 2020 The four-parameter logistic (4PL) model has recently attracted much interest in educational testing and psychological measurement. This paper develops a new Gibbs-slice sampling algorithm for estimating the 4PL model parameters in a fully Bayesian ...
Jiwei Zhang +5 more
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A Dirichlet Process Prior Approach for Covariate Selection
Entropy, 2020 The variable selection problem in general, and specifically for the ordinary linear regression model, is considered in the setup in which the number of covariates is large enough to prevent the exploration of all possible models.
Stefano Cabras
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Journal of Machine Learning Research, 2013 The Gibbs sampler is one of the most popular algorithms for inference in statistical models. In this paper, we introduce a herding variant of this algorithm, called herded Gibbs, that is entirely deterministic. We prove that herded Gibbs has an $O(1/T)$ convergence rate for models with independent variables and for fully connected probabilistic ...
Bornn, L. +5 more
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Clustering of Conditional Mutual Information for Quantum Gibbs States above a Threshold Temperature [PDF]
, 2020 We prove that the quantum Gibbs states of spin systems above a certain threshold temperature are approximate quantum Markov networks, meaning that the conditional mutual information decays rapidly with distance.
Brandão, Fernando G. S. L. +2 more
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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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Fast Gibbs sampling for high-dimensional Bayesian inversion [PDF]
, 2016 Solving ill-posed inverse problems by Bayesian inference has recently attracted considerable attention. Compared to deterministic approaches, the probabilistic representation of the solution by the posterior distribution can be exploited to explore and ...
Burger M +15 more
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Gibbs Sampling Subjectively Interesting Tiles [PDF]
, 2020 The local pattern mining literature has long struggled with the so-called pattern explosion problem: the size of the set of patterns found exceeds the size of the original data. This causes computational problems (enumerating a large set of patterns will inevitably take a substantial amount of time) as well as problems for interpretation and usability (
Bendimerad, Anes +4 more
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Continuous Herded Gibbs Sampling
2021 IEEE 24th International Conference on Information Fusion (FUSION), 2021 Herding is a technique to sequentially generate deterministic samples from a probability distribution. In this work, we propose a continuous herded Gibbs sampler that combines kernel herding on continuous densities with the Gibbs sampling idea. Our algorithm allows for deterministically sampling from high-dimensional multivariate probability densities,
Wolf, Laura M., Baum, Marcus
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