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Questions tagged [gibbs]

The Gibbs sampler is a simple form of Markov Chain Monte Carlo simulation, widely used in Bayesian statistics, based on sampling from full conditional distributions for each variable or group of variables. The name comes from the method being first used on Gibbs random fields modeling of images by Geman and Geman (1984).

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JAGS: posterior predictive distribution

I am fitting a simple linear regression model with R & JAGS: ...
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Sampling covariance matrix using Gibbs sampling

I am sampling covariance matrix from a Inverse Wishart distribution. In one dimensional case, after doing sufficient iterations I am taking the mode value for variance (after removing the burn-in ...
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Posterior expression for Gibbs sampling

I am trying to estimate parameters of a two dimensional Normal distribution using Gibbs sampling. While it was very easy transform the posterior equation for mean vector to a single dimension normal ...
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Comparison of Slice sampling and Gibbs sampling

To me, the two are similar in the sense that slice sampling is just Gibbs sampling for the uniform distribution over the area under the plot of the density function. Is that right? I was wondering if ...
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Where do the full conditionals come from in Gibbs sampling?

MCMC algorithms like Metropolis-Hastings and Gibbs sampling are ways of sampling from the joint posterior distributions. I think I understand and can implement metropolis-hasting pretty easily--you ...