摘要: The Markov chain Monte Carlo (MCMC) revolution sweeping statistics is drastically changing how statisticians perform integration and summation. In particular, the Metropolis algorithm Gibbs sampling make it straightforward to construct a that samples from complicated conditional distribution. Once sample available, then any expectation can be approximated by forming its corresponding average. implications of this insight are profound for both classical Bayesian statistics. As bonus, trivial changes yield simulated annealing, general-purpose solving difficult combinatorial optimization problems.
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