The Metropolis-Hastings-Green Algorithm
نویسنده
چکیده
1.1 Dimension Changing The Metropolis-Hastings-Green algorithm (as opposed to just MetropolisHastings with no Green) is useful for simulating probability distributions that are a mixture of distributions having supports of different dimension. An early example (predating Green’s general formulation) was an MCMC algorithm for simulating spatial point processes (Geyer and Møller, 1994). More widely used examples are Bayesian change point models and Bayesian model selection (Green, 1995). Abstractly, we are interested in a Markov chain having a state space that is a union of Euclidean spaces of different dimension. Let X denote the state space of the Markov chain. This is assumed to be a disjoint union
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