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Title: A Theory for Dynamic Weighting in Monte Carlo Computation
Authors: Liu, J.S.
Liang, F. 
Wong, W.H.
Keywords: Gibbs sampling
Importance sampling
Ising model
Metropolis algorithm
Neural network
Renewal theory
Simulated annealing
Simulated tempering
Issue Date: Jun-2001
Citation: Liu, J.S.,Liang, F.,Wong, W.H. (2001-06). A Theory for Dynamic Weighting in Monte Carlo Computation. Journal of the American Statistical Association 96 (454) : 561-573. ScholarBank@NUS Repository.
Abstract: This article provides a first theoretical analysis of a new Monte Carlo approach, the dynamic weighting algorithm, proposed recently by Wong and Liang. In dynamic weighting Monte Carlo, one augments the original state space of interest by a weighting factor, which allows the resulting Markov chain to move more freely and to escape from local modes. It uses a new invariance principle to guide the construction of transition rules. We analyze the behavior of the weights resulting from such a process and provide detailed recommendations on how to use these weights properly. Our recommendations are supported by a renewal theory-type analysis. Our theoretical investigations are further demonstrated by a simulation study and applications in neural network training and Ising model simulations.
Source Title: Journal of the American Statistical Association
ISSN: 01621459
Appears in Collections:Staff Publications

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