Abstract
This article contains an overview of the literature concerning the computational complexity of Metropolis-Hastings based MCMC methods for sampling probability measures on Rd , 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?. © Springer-Verlag Berlin Heidelberg 2009.
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CITATION STYLE
Beskos, A., & Stuart, A. (2009). Computational complexity of metropolis-hastings methods in high dimensions. In Monte Carlo and Quasi-Monte Carlo Methods 2008 (pp. 61–71). Springer Verlag. https://doi.org/10.1007/978-3-642-04107-5_4
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