Comment: Lancaster Probabilities and Gibbs Sampling
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چکیده
منابع مشابه
Comment: Lancaster Probabilities and Gibbs Sampling
It is a pleasure to congratulate the authors for this excellent, original and pedagogical paper. I read a preliminary draft at the end of 2006 and I then mentioned to the authors that their work should be set within the framework of Lancaster probabilities, a remoted corner of the theory of probability, now described in their Section 6.1. The reader is referred to Lancaster (1958, 1963, 1975) a...
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Patrizia Berti is Professor, Dipartimento di Matematica Pura ed Applicata “G. Vitali”, Universita’ di Modena e Reggio-Emilia, via Campi 213/B, 41100 Modena, Italy e-mail: [email protected]. Guido Consonni is Professor, Dipartimento di Economia Politica e Metodi Quantitativi, Universita’ di Pavia, via S. Felice 5, 27100 Pavia, Italy e-mail: [email protected]. Luca Pratelli is Profe...
متن کاملComment: Gibbs Sampling, Exponential Families, and Orthogonal Polynomials
It is our pleasure to congratulate the authors (hereafter DKSC) on an interesting paper that was a delight to read. While DKSC provide a remarkable collection of connections between different representations of the Markov chains in their paper, we will focus on the “running time analysis” portion. This is a familiar problem to statisticians; given a target population, how can we obtain a repres...
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The particle Gibbs sampler is a Markov chain Monte Carlo (MCMC) algorithm to sample from the full posterior distribution of a state-space model. It does so by executing Gibbs sampling steps on an extended target distribution defined on the space of the auxiliary variables generated by an interacting particle system. This paper makes the following contributions to the theoretical study of this a...
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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 graphical models. Herded Gibbs is shown to outperfo...
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ژورنال
عنوان ژورنال: Statistical Science
سال: 2008
ISSN: 0883-4237
DOI: 10.1214/08-sts252a