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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