A quasi-Monte Carlo Metropolis algorithm.
This work presents a version of the Metropolis-Hastings algorithm using quasi-Monte Carlo inputs. We prove that the method yields consistent estimates in some problems with finite state spaces and completely uniformly distributed inputs. In some numerical examples, the proposed method is much more accurate than ordinary Metropolis-Hastings sampling.
Owen
Art B
AB
Department of Statistics, Stanford University, Stanford, CA 94305, USA. owen@stat.stanford.edu
Tribble
Seth D
SD
D000465:Algorithms; D003205:Computing Methodologies; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method