Computational Complexity of Metropolis-Hastings Methods in High Dimensions

Alexandros Beskos () and Andrew Stuart
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Alexandros Beskos: University College of London, Department of Statistical Science

A chapter in Monte Carlo and Quasi-Monte Carlo Methods 2008, 2009, pp 61-71 from Springer

Abstract: Abstract This article contains an overview of the literature concerning the computational complexity of Metropolis-Hastings based MCMC methods for sampling probability measures on ℝ d , when the dimension d is large. The material is structured in three parts addressing, in turn, the following questions: (i) what are sensible assumptions to make on the family of probability measures indexed by d ? (ii) what is known concerning computational complexity for Metropolis-Hastings methods applied to these families? (iii) what remains open in this area?

Keywords: Markov Chain; Invariant Measure; MCMC Method; Target Distribution; Acceptance Probability (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-04107-5_4

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DOI: 10.1007/978-3-642-04107-5_4

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