Dynamically weighted importance sampling in Monte Carlo computation

Journal of the American Statistical Association

Volumn 97, Issue 459, 2002, Pages 807-821

  Liang, Faming ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Dynamic weighting; Markov chain Monte Carlo; Metropolis Hastings algorithm; Sequential importance sampling

Indexed keywords

EID: 0036744537     PISSN: 01621459     EISSN: None     Source Type: Journal    
DOI: 10.1198/016214502388618618     Document Type: Article

References (34)

1. Barker, A.A. (1965, "Monte Carlo Calculations of the Radial Distribution Functions for a Protonelectron Plasma," Australian Journal of Physics, 18, 119-133.
2. Bastolla, U., Frauenkron, H., Gerstner, E., Grassberger, P., and Nadler, W. (1998), "Testing a New Monte Carlo Algorithm for Protein Folding," Proteins: Structure, Function, and Genetics, 32, 52-66.
3. Berg, B.A., and Neuhaus, T. (1991), "Multicanonical Algorithms for First-Order Phase Transitions," Physics Letters B, 267, 249-253.
4. Gelfand, A.E., and Smith, A.F.M. (1990), "Sampling-Based Approaches to Calculating Marginal Densities," Journal of the American Statistical Association, 85, 398-409.
5. Geman, S., and Geman, D. (1984), "Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration of Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741.
6. Geweke, J. (1989), "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, 57, 1317-1339.
7. Geyer, C.J. (1991), "Markov Chain Monte Carlo Maximum Likelihood," in Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface, ed. E.M. Keramigas, Fairfax, VA: Interface Foundation, pp. 156-163.
8. Geyer, C.J., and Thompson, E.A. (1995), "Annealing Markov Chain Monte Carlo With Applications to Pedigree Analysis," Journal of the American Statistical Association, 90, 909-920.
9. Goodman, J., and Sokal, A.D. (1989), "Multigrid Monte Carlo Method: Conceptual Foundations, "Physical Review D, 40, 2035-2071.
10. Grassberger, P. (1997), "Pruned-Enriched Rosenbluth Method: Simulation of θ Polymers of Chain Length up to 1,000,000," Physical Review E, 3682-3693.
11. Green, P. J. (1995), "Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination," Biometrika, 82, 711-732.
12. Hammersley, J. M., and Handscomb, D. C. (1964), Monte Carlo Methods, London: Methuen.
13. Hastings, W. K. (1970), "Monte Carlo Sampling Methods Using Markov Chain and Their Applications," Biometrika, 57, 97-109.
14. Kirkpatrick, S., Gelatt, C.D. Jr., and Vecchi, M.P. (1983), "Optimization by Simulated Annealing," Science, 220, 671-680.
15. Lawrence, C.E., Altschul, S.F., Boguski, M.S., Liu, J.S., Neuwald, A.F., and Wootton, J.C. (1993), "Detecting Subtle Sequence Signals: A Gibbs Sampling Strategy for Multiple Alignment," Science, 262, 208-214.
16. p Model Sampling and Change-Point Problems," Statistics Sinica, 10, 317-342.
17. _ (2001), "Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models," Journal of the American Statistical Association, 96, 653-666.
18. Liu, J.S., and Chen, R. (1998), "Sequential Monte Carlo Methods for Dynamic Systems," Journal of the American Statistical Association, 93, 1032-1044.
19. Liu, J. S., Chen, R., and Logvinenko, T. (2001), "A Theoretical Framework for Sequential Importance Sampling and Resampling," in Sequential Monte Carlo in Practice, Springer-Verlag.
20. Liu, J.S., Chen, R., and Wong, W.H. (1998), "Rejection Control and Sequential Importance Sampling," Journal of the American Statistical Association, 93, 1022-1031.
21. Liu, J.S., Liang, F., and Wong, W.H. (2000), "A Theory for Dynamic Weighting in Monte Carlo," Journal of the American Statistical Association, 96, 561-573.
22. Liu, J. S., Wong, W. H., and Kong, A. (1994), "Covariance Structure of the Gibbs Sampler With Applications to the Comparisons of Estimators and Augmentation Schemes," Biometrika, 81, 27-40.
23. Marinari, E., and Parisi, G. (1992), "Simulated Tempering: A New Monte Carlo Scheme," Europhysics Letters, 19, 451-458.
24. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., and Teller, E. (1953), "Equation of State Calculations by Fast Computing Machines," Journal of Chemical Physics, 21, 1087-1091.
25. MacEachern, S.N., Clyde, M., and Liu, J.S. (1999), "Sequential Importance Sampling for Non-Parametric Bayes Models: The Next Generation," Canadian Journal of Statistics, 27, 251-267.
26. Neal, R.M. (2001), "Annealed Importance Sampling," Statistics and Computing, 11, 125-139.
27. Peskun, P.H. (1973), "Optimum Monte Carlo Sampling Using Markov Chains," Biometrika, 60, 607-612.
28. Ripley, B.D. (1987), Stochastic Simulation, New York: Wiley.
29. Ritter, C., and Tanner, M.A. (1992), "Facilitating the Gibbs Sampler: The Gibbs Stopper and the Griddy-Gibbs Sampler," Journal of the American Statistical Association, 87, 861-868.
30. Roberts, G.O. (1996), "Markov Chain Concepts Related to Sampling Algorithms," in Markov Chain Monte Carlo in Practice, eds. W.R. Gilks, S. Richardson, and D.J. Spiegelhalter, London: Chapman and Hall, pp. 45-74.
31. Rosenbluth, M.N., and Rosenbluth, A.W. (1955), "Monte Carlo Calculation of the Average Extension of Molecular Chains," Journal of Chemical Physics, 23, 256.
32. Swendsen, R.H., and Wang, J.S. (1987), "Nonuniversal Critical Dynamics in Monte Carlo Simulations," Physics Review Letters, 58, 86-88.
33. Tanner, M.A., and Wong, W.H. (1987), "The Calculation of Posterior Distribution by Data Augmentation" (with discussion), Journal of the American Statistical Association, 82, 528-550.
34. Wong, W.H., and Liang, F. (1997), "Dynamic Weighting in Monte Carlo and Optimization," Proceedings of the National Academy of Science USA, 94, 14220-14224.