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951
Adaptive Gibbs samplers
, 2010
"... We consider various versions of adaptive Gibbs and Metropolis-within-Gibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on the fly during a run, by learning as they go in an attempt to optimise the algorithm. We present a cautionary example of ..."
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We consider various versions of adaptive Gibbs and Metropolis-within-Gibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on the fly during a run, by learning as they go in an attempt to optimise the algorithm. We present a cautionary example
Application of a Gibbs sampler . . .
, 2008
"... This research is devoted to studying statistical inference implemented using the Gibbs Sampler for a hierarchical Bayesian linear model with first order autoregressive structure. This model was applied to global-mean monthly temperatures from January 1880 to April 2008 and used to estimate a time tr ..."
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This research is devoted to studying statistical inference implemented using the Gibbs Sampler for a hierarchical Bayesian linear model with first order autoregressive structure. This model was applied to global-mean monthly temperatures from January 1880 to April 2008 and used to estimate a time
On reparametrization and the Gibbs sampler
, 2013
"... Gibbs samplers derived under different parametrizations of the target density can have radically different rates of convergence. In this article, we specify conditions under which reparametrization leaves the convergence rate of a Gibbs chain unchanged. An example illus-trates how these results can ..."
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Cited by 3 (3 self)
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Gibbs samplers derived under different parametrizations of the target density can have radically different rates of convergence. In this article, we specify conditions under which reparametrization leaves the convergence rate of a Gibbs chain unchanged. An example illus-trates how these results can
Subsampling the Gibbs Sampler
"... INTRODUCTION Markov chain Monte Carlo methods have enjoyed a surge of interest since Gelfand and Smith (1990) described the Gibbs sampler and its effectiveness in providing approximate Bayesian solutions for models that had previously been approachable only with great difficulty, or that had been d ..."
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Cited by 24 (0 self)
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INTRODUCTION Markov chain Monte Carlo methods have enjoyed a surge of interest since Gelfand and Smith (1990) described the Gibbs sampler and its effectiveness in providing approximate Bayesian solutions for models that had previously been approachable only with great difficulty, or that had been
On the Geometric Convergence of the Gibbs Sampler
- Journal of the Royal Statistical Society, Series B
, 1994
"... This paper investigates conditions under which the Gibbs sampler (Gelfand and Smith, 1990; Tanner and Wong, 1987; Geman and Geman, 1984) converges at a geometric rate. The main results appear in Sections 2 and 3, where geometric convergence results are established, with respect to total variation an ..."
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Cited by 43 (6 self)
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This paper investigates conditions under which the Gibbs sampler (Gelfand and Smith, 1990; Tanner and Wong, 1987; Geman and Geman, 1984) converges at a geometric rate. The main results appear in Sections 2 and 3, where geometric convergence results are established, with respect to total variation
Information bounds for Gibbs samplers
- In preparation
, 1995
"... If we wish to efficiently estimate the expectation of an arbitrary function on the basis of the output of a Gibbs sampler, which is better: deterministic or random sweep? In each case we calculate the asymptotic variance of the empirical estimator, the average of the function over the output, and de ..."
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Cited by 3 (2 self)
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If we wish to efficiently estimate the expectation of an arbitrary function on the basis of the output of a Gibbs sampler, which is better: deterministic or random sweep? In each case we calculate the asymptotic variance of the empirical estimator, the average of the function over the output
How Many Iterations in the Gibbs Sampler?
- In Bayesian Statistics 4
, 1992
"... When the Gibbs sampler is used to estimate posterior distributions (Gelfand and Smith, 1990), the question of how many iterations are required is central to its implementation. When interest focuses on quantiles of functionals of the posterior distribution, we describe an easily-implemented metho ..."
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Cited by 159 (6 self)
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When the Gibbs sampler is used to estimate posterior distributions (Gelfand and Smith, 1990), the question of how many iterations are required is central to its implementation. When interest focuses on quantiles of functionals of the posterior distribution, we describe an easily
Rates of Convergence for the Gibbs Sampler
, 1997
"... This work concerns the rate of convergence for the Gibbs sampler (Glauber type dynamics) on configuration space S , where the index set is a finite subset of ZZ d with cardinality n, and the single-spin space S is a finite set with cardinality s. Under the Dobrushin-Shlosman condition, an expli ..."
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This work concerns the rate of convergence for the Gibbs sampler (Glauber type dynamics) on configuration space S , where the index set is a finite subset of ZZ d with cardinality n, and the single-spin space S is a finite set with cardinality s. Under the Dobrushin-Shlosman condition
1 The Gibbs Sampler
, 2011
"... This section describes the detailed Gibbs sampler for Bayesian estimation. For all notation, please see the original paper. 1.1 Generating Lkt and Ct To generate Lkt and Ct, we …rst transform the dynamic factor model into a state space form. 0 Let Djt = Djt Zt. The measurement equation of the model ..."
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This section describes the detailed Gibbs sampler for Bayesian estimation. For all notation, please see the original paper. 1.1 Generating Lkt and Ct To generate Lkt and Ct, we …rst transform the dynamic factor model into a state space form. 0 Let Djt = Djt Zt. The measurement equation of the model
Results 1 - 10
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951
