Christian Robert on the amazing Gibbs sampler

Professor Christian Robert remarks on the amazing Gibbs sampler. Implicitly he’s also underscoring the power of properly done Bayesian computational analysis. For here we have a problem with a posterior distribution having two strong modes, so a point estimate, like a mean would be completely wrong, yet that’s what MLE would give you. Gibbs hangs in there, acknowledging both modes.

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This entry was posted in Bayes, Bayesian, BUGS, Gibbs Sampling, JAGS, mathematics, maths, MCMC, probabilistic programming, rationality, statistics, stochastic algorithms, stochastic search. Bookmark the permalink.

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