A Theory for Dynamic Weighting in Monte Carlo Computation

@inproceedings{Jun2001ATF,
  title={A Theory for Dynamic Weighting in Monte Carlo Computation},
  author={Sukky Jun and Faming and Wing Sze Hung},
  year={2001}
}
This article provides a Ž rst 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… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 30 references

Weighted Markov Chain Monte Carlo and Optimization,” unpublished doctorial thesis, The Chinese University of Hong Kong

F. Liang
1997
View 8 Excerpts
Highly Influenced

Applied Probability and Queues, New York: Wiley

S. Asmussen
1987
View 4 Excerpts
Highly Influenced

Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

IEEE Transactions on Pattern Analysis and Machine Intelligence • 1984
View 5 Excerpts
Highly Influenced

Dynamic weighting in Monte Carlo and optimization.

Proceedings of the National Academy of Sciences of the United States of America • 1997
View 6 Excerpts
Highly Influenced

Large Deviations Techniques, Boston: Jones and Bartlett

A. Dembo, O. Zeitouni
1993
View 2 Excerpts
Highly Influenced

The Multiple-try Method and Local Optimization in Metropolis Sampling,

J. S. Liu, F. Liang, W. H. Wong
Journal of the American Statistical Association, • 2000
View 1 Excerpt

Markovian Structures in Biological Sequence Alignments,

J. S. Liu, A. F. Neuwald, C. E. Lawrence
Journal of the American Statistical Association, • 1999
View 1 Excerpt

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