منابع مشابه
Variable Selection Via Gibbs Sampling
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Gibbs sampling is a widely applicable inference technique that can in principle deal with complex multimodal distributions. Unfortunately, it fails in many practical applications due to slow convergence and abundance of local minima. In this paper, we propose a general method of speeding up Gibbs sampling in probabilistic models. The method works by introducing auxiliary variables which represe...
متن کاملGibbs Posterior for Variable Selection in High-dimensional Classification and Data Mining
In the popular approach of “Bayesian variable selection” (BVS), one uses prior and posterior distributions to select a subset of candidate variables to enter the model. A completely new direction will be considered here to study BVS with a Gibbs posterior originating in statistical mechanics. The Gibbs posterior is constructed from a risk function of practical interest (such as the classificati...
متن کاملGibbs Posterior for Variable Selection in High-dimensional Classification and Data Mining1
In the popular approach of “Bayesian variable selection” (BVS), one uses prior and posterior distributions to select a subset of candidate variables to enter the model. A completely new direction will be considered here to study BVS with a Gibbs posterior originating in statistical mechanics. The Gibbs posterior is constructed from a risk function of practical interest (such as the classificati...
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Latent class analysis is used to perform model based clustering formultivariate categorical responses. Selection of the variables most relevant for clustering is an important task which can affect the quality of clustering considerably. This work considers a Bayesian approach for selecting the number of clusters and the best clustering variables. The main idea is to reformulate the problem of g...
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ژورنال
عنوان ژورنال: Journal of Statistical Software
سال: 2002
ISSN: 1548-7660
DOI: 10.18637/jss.v007.i07