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An Efficient Gibbs Sampler for Structural Inference in Bayesian Networks. CRiSM Working Paper 1121
 Dept. of Statistics, University of Warwick). Friedman, N. (2004) Science
, 2011
"... We propose a Gibbs sampler for structural inference in Bayesian networks. The standard Markov chain Monte Carlo (MCMC) algorithms used for this problem are randomwalk MetropolisHastings samplers, but for problems of even moderate dimension, these samplers often exhibit slow mixing. The Gibbs samp ..."
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Cited by 1 (1 self)
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We propose a Gibbs sampler for structural inference in Bayesian networks. The standard Markov chain Monte Carlo (MCMC) algorithms used for this problem are randomwalk MetropolisHastings samplers, but for problems of even moderate dimension, these samplers often exhibit slow mixing. The Gibbs
CIRJEF481 Efficient Gibbs Sampler for Bayesian Analysis of a Sample Selection Model
, 2007
"... CIRJE Discussion Papers can be downloaded without charge from: ..."
Gibbs Sampling Methods for StickBreaking Priors
"... ... In this paper we present two general types of Gibbs samplers that can be used to fit posteriors of Bayesian hierarchical models based on stickbreaking priors. The first type of Gibbs sampler, referred to as a Polya urn Gibbs sampler, is a generalized version of a widely used Gibbs sampling meth ..."
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Cited by 388 (19 self)
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... In this paper we present two general types of Gibbs samplers that can be used to fit posteriors of Bayesian hierarchical models based on stickbreaking priors. The first type of Gibbs sampler, referred to as a Polya urn Gibbs sampler, is a generalized version of a widely used Gibbs sampling
The Generalized Gibbs Sampler and the Neighborhood Sampler
"... The Generalized Gibbs Sampler (GGS) is a recently proposed Markov chain Monte Carlo (MCMC) technique that is particularly useful for sampling from distributions defined on spaces in which the dimension varies from point to point or in which points are not easily defined in terms of coordinates. Suc ..."
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, provides an alternative that is easy to implement and often highly efficient. The GGS provides a very general framework for MCMC simulation. Not only the conventional Gibbs Sampler, but also a variety of other well known samplers emerge as special cases. These include the MetropolisHastings sampler
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
On the particle Gibbs sampler
, 2013
"... Abstract. The particle Gibbs sampler is a Markov chain Monte Carlo (MCMC) algorithm which operates on the extended space of the auxiliary variables generated by an interacting particle system. In particular, it samples the discrete variables that determine the particle genealogy. We propose a coupli ..."
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Cited by 3 (0 self)
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Abstract. The particle Gibbs sampler is a Markov chain Monte Carlo (MCMC) algorithm which operates on the extended space of the auxiliary variables generated by an interacting particle system. In particular, it samples the discrete variables that determine the particle genealogy. We propose a
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 easilyimplemented 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
A Gibbs sampler for inequalityconstrained geostatistical interpolation and inverse modeling
"... [1] Interpolation and inverse modeling problems are ubiquitous in environmental sciences. In many applications, the parameters being estimated or mapped have physical constraints, such as nonnegativity (e.g. concentration, hydraulic conductivity), solubility limits, censored data (e.g. due to dry we ..."
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and computationally efficient Gibbs sampler, a Markov chain Monte Carlo technique, based on an a priori truncated Gaussian distribution model, which allows for multiple and variable physical constraints to be enforced within a geostatistical framework. Sample interpolation and inverse modeling applications confirm
Results 1  10
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