Gibbs Sampling
Cycles through parameters, drawing each from its full conditional distribution given the current values of all others; every update is a Metropolis-Hastings step that is always accepted, but strong posterior correlations make the coordinate-wise moves mix slowly.
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Cycles through parameters, drawing each from its full conditional distribution given the current values of all others; every update is a Metropolis-Hastings step that is always accepted, but strong posterior correlations make the coordinate-wise moves mix slowly.
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