Geometric convergence of the Metropolis-Hastings simulation algorithm
نویسندگان
چکیده
منابع مشابه
A Geometric Interpretation of the Metropolis–Hastings Algorithm
The Metropolis-Hastings algorithm transforms a given stochastic matrix into a reversible stochastic matrix with a prescribed stationary distribution. We show that this transformation gives the minimum distance solution in an L1 metric.
متن کاملGeometric Convergence of the Metropolis Hastings Simulation Algorithm Lars Holden Norwegian Computing Center and University of Oslo
Necessary and su cient conditions for geometric con vergence in the relative supremum norm of the Metropolis Hastings simulation algorithm with a general generating function are estab lished An explicit expression for the convergence rate is given Introduction This paper discusses the convergence rate for the Metropolis Hastings simulation algorithm proposed in Hastings The Metropolis Hastings ...
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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 wide...
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We consider Markov chain Monte Carlo algorithms which combine Gibbs updates with Metropolis-Hastings updates, resulting in a conditional Metropolis-Hastings sampler (CMH). We develop conditions under which the CMH will be geometrically or uniformly ergodic. We illustrate our results by analysing a CMH used for drawing Bayesian inferences about the entire sample path of a diffusion process, base...
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ژورنال
عنوان ژورنال: Statistics & Probability Letters
سال: 1998
ISSN: 0167-7152
DOI: 10.1016/s0167-7152(98)00096-0