Discussion
What is Markov chain Monte Carlo sampling in a network?
Answer: D
Gibbs sampling starts from an arbitrary assignment and repeatedly resamples one variable given its blanket, and the sequence converges to the posterior. The samples are dependent, which is the price of avoiding rejection. Complexity ladders and independent sampling are different schemes. That a local update rule produces globally correct samples in the limit is a remarkable result and it is what makes the method practical for large networks.
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