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The American Statistician, 1994
Abstract This article provides a justification of the ban against sub-sampling the output of a stationary Markov chain that is suitable for presentation in undergraduate and beginning graduate-level courses. The justification does not rely on reversibility of the chain as does Geyer's (1992) argument and so applies to the usual implementation of the ...
Steven N. Maceachern, L. Mark Berliner
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Abstract This article provides a justification of the ban against sub-sampling the output of a stationary Markov chain that is suitable for presentation in undergraduate and beginning graduate-level courses. The justification does not rely on reversibility of the chain as does Geyer's (1992) argument and so applies to the usual implementation of the ...
Steven N. Maceachern, L. Mark Berliner
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1999
The previous chapter developed simulation techniques that could be called “generic,” since they require only a limited amount of information about the distribution to be simulated. For example, the generic algorithm ARMS (§6.3.3) aims at reproducing the density f of this distribution in an automatic manner.
Christian P. Robert, George Casella
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The previous chapter developed simulation techniques that could be called “generic,” since they require only a limited amount of information about the distribution to be simulated. For example, the generic algorithm ARMS (§6.3.3) aims at reproducing the density f of this distribution in an automatic manner.
Christian P. Robert, George Casella
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2004
The proposal transition T(x,y) in a Metropolis sampler is often an arbitrary choice out of convenience. In many applications, the proposal is chosen to be a locally uniform move. In fact, the use of symmetric and locally uniform proposals is so prevailing that these are often referred to as “unbiased proposals” in the literature.
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The proposal transition T(x,y) in a Metropolis sampler is often an arbitrary choice out of convenience. In many applications, the proposal is chosen to be a locally uniform move. In fact, the use of symmetric and locally uniform proposals is so prevailing that these are often referred to as “unbiased proposals” in the literature.
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1991
To motivate the Gibbs Sampler, we consider a modification of Data Augmentation which we will refer to as Chained Data Augmentation. The Gibbs Sampler turns out to be a multivariate extension of Chained Data Augmentation.
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To motivate the Gibbs Sampler, we consider a modification of Data Augmentation which we will refer to as Chained Data Augmentation. The Gibbs Sampler turns out to be a multivariate extension of Chained Data Augmentation.
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On convergence of the EM algorithmand the Gibbs sampler
Statistics and Computing, 1999In this article we investigate the relationship between the EM algorithm and the Gibbs sampler. We show that the approximate rate of convergence of the Gibbs sampler by Gaussian approximation is equal to that of the corresponding EM-type algorithm. This helps in implementing either of the algorithms as improvement strategies for one algorithm can be ...
Sujit K. Sahu, Gareth O. Roberts
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Gibbs sampler to stochastic volatility models
2001 European Control Conference (ECC), 2001A new technique for nonlinear state and parameter estimation of the discrete time stochastic volatility models in which the logarithm of the asset return conditional variance follows an autoregressive model has been developed. The Gibbs sampling algorithm is used to construct a Markov-chain simulation tool that reflects both inherent model variability ...
Miroslav Simandl, Tomás Soukup
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A new Gibbs sampler for Bayesian lasso
Communications in Statistics - Simulation and Computation, 2018Lasso regression, a special case of Bridge regression of a penalty function ∑|βj|q with q = 1, is considered from a Bayesian perspective.
Rahim Alhamzawi +1 more
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Evolutionary gibbs sampler for image segmentation
2004 International Conference on Image Processing, 2004. ICIP '04., 2005We propose a novel evolutionary algorithm for the function optimization problem in Bayesian image segmentation with Markov random field prior. Function variables are partitioned into several codings. A pivot coding is selected and variables in it are evolved respectively according to their probability distributions which encode both the evolutionary ...
Xiao Wang, Han Wang 0001
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Partially Collapsed Gibbs Samplers
Journal of the American Statistical Association, 2008Ever-increasing computational power, along with ever–more sophisticated statistical computing techniques, is making it possible to fit ever–more complex statistical models. Among the more computationally intensive methods, the Gibbs sampler is popular because of its simplicity and power to effectively generate samples from a high-dimensional ...
van Dyk, David A., Park, Taeyoung
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On the Particle Gibbs Sampler [PDF]
, 2013 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.
Nicolas Chopin, Sumeetpal S. Singh
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