Computational Complexity of Metropolis-Hastings Methods in High Dimensions
نویسنده
چکیده
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?
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تاریخ انتشار 2010