1 The MetropolisHastings algorithm is a member of the family; as such, it is designed to generate...

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1 The Metropolis–Hastings algorithm is a member of the family; as such, it is designed to generate samples x (eventually) according to target probabilities (x). (Typically we are interested in sampling from (x)=P(x | e).) Like simulated annealing, Metropolis–Hastings operates in two stages. First, it samples a new state x′ from a proposal distribution q(x′| x), given the current state x. Then, it probabilistically accepts or rejects x′ according to ACCEPTANCE the acceptance probability

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