Please use this identifier to cite or link to this item: https://doi.org/10.1198/016214502388618618
Title: Dynamically weighted importance sampling in Monte Carlo computation
Authors: Liang, F. 
Keywords: Dynamic weighting
Markov chain Monte Carlo
Metropolis-Hastings algorithm
Sequential importance sampling
Issue Date: Sep-2002
Citation: Liang, F. (2002-09). Dynamically weighted importance sampling in Monte Carlo computation. Journal of the American Statistical Association 97 (459) : 807-821. ScholarBank@NUS Repository. https://doi.org/10.1198/016214502388618618
Abstract: This article describes a new Monte Carlo algorithm, dynamically weighted importance sampling (DWIS), for simulation and optimization. In DWIS, the state of the Markov chain is augmented to a population. At each iteration, the population is subject to two move steps, dynamic weighting and population control. These steps ensure that DWIS can move across energy barriers like dynamic weighting, but with the weights well controlled and with a finite expectation. The estimates can converge much faster than they can with dynamic weighting. A generalized theory for importance sampling is introduced to justify the new algorithm. Numerical examples are given to show that dynamically weighted importance sampling can perform significantly better than the Metropolis-Hastings algorithm and dynamic weighting in some situations.
Source Title: Journal of the American Statistical Association
URI: http://scholarbank.nus.edu.sg/handle/10635/105099
ISSN: 01621459
DOI: 10.1198/016214502388618618
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