메뉴 건너뛰기




Volumn 96, Issue 454, 2001, Pages 653-666

Real-parameter evolutionary monte carlo with applications to bayesian mixture models

Author keywords

Crossover; Evolutionary Monte Carlo; Exchange; Genetic algorithm; Markov chain Monte Carlo; Metropolis algorithm; Mixture model; Mutation; Neural network; Parallel tempering

Indexed keywords


EID: 1542573405     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1198/016214501753168325     Document Type: Article
Times cited : (171)

References (44)
  • 1
    • 0000106469 scopus 로고
    • Multicanonical Algorithms for First Order Phase Transitions
    • Ser. B
    • Berg, B. A., and Neuhaus, T. (1991), “Multicanonical Algorithms for First Order Phase Transitions,” Physics Letters, Ser. B, 267, 249–253.
    • (1991) Physics Letters , vol.267 , pp. 249-253
    • Berg, B.A.1    Neuhaus, T.2
  • 3
    • 0001561263 scopus 로고
    • Bayesian Back-Propagation
    • Buntine, W. L., and Weigend, A. S. (1991), “Bayesian Back-Propagation,” Complex Systems 5, 603–643.
    • (1991) Complex Systems , vol.5 , pp. 603-643
    • Buntine, W.L.1    Weigend, A.S.2
  • 4
    • 0000506629 scopus 로고
    • Bayesian Model Choice via Markov Chain Monte Carlo
    • Ser. B
    • Carlin, B., and Chib, S. (1993), “Bayesian Model Choice via Markov Chain Monte Carlo,” Journal of the Royal Statistical Society, Ser. B, 57, 473–484.
    • (1993) Journal of the Royal Statistical Society , vol.57 , pp. 473-484
    • Carlin, B.1    Chib, S.2
  • 6
    • 0041974049 scopus 로고
    • Marginal Likelihood From the Gibbs Output
    • Chib, S. (1995), “Marginal Likelihood From the Gibbs Output,” Journal of American Statistical Association, 90, 1313–1321.
    • (1995) Journal of American Statistical Association , vol.90 , pp. 1313-1321
    • Chib, S.1
  • 7
    • 0024861871 scopus 로고
    • Approximations by Superpositions of a Sigmoidal Function
    • Cybenko, G. (1989), “Approximations by Superpositions of a Sigmoidal Function,” Mathematics of Control, Signals and Systems, 2, 303–314.
    • (1989) Mathematics of Control, Signals and Systems , vol.2 , pp. 303-314
    • Cybenko, G.1
  • 8
    • 0003488043 scopus 로고    scopus 로고
    • Neural Network Based Models for Forecasting
    • J. G. Taylor, New York: Wiley
    • Ding, X., Canu, S., and Denoeux, T. (1996), “Neural Network Based Models for Forecasting,” in Neural Networks and Their Applications, ed. J. G. Taylor, New York: Wiley, pp. 153–167.
    • (1996) Neural Networks and Their Applications , pp. 153-167
    • Ding, X.1    Canu, S.2    Denoeux, T.3
  • 9
    • 0000308566 scopus 로고
    • Real-Coded Genetic Algorithms and Interval-Schematai
    • G. J. E. Rawlins, San Mateo, CA: Morgan Kaufmann
    • Eshelman, L. J., and Schaffer, J. D. (1993), “Real-Coded Genetic Algorithms and Interval-Schematai,” in Foundation of Genetic Algorithms 2, ed. G. J. E. Rawlins, San Mateo, CA: Morgan Kaufmann, pp. 187–202.
    • (1993) Foundation of Genetic Algorithms , vol.2 , pp. 187-202
    • Eshelman, L.J.1    Schaffer, J.D.2
  • 10
    • 0000954353 scopus 로고    scopus 로고
    • Efficient Metropolis Jumping Rules
    • J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, New York: Oxford University Press
    • Gelman, A., Roberts, R. O., and Gilks, W. R. (1996), “Efficient Metropolis Jumping Rules,” in Bayesian Statistics 5, eds. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, New York: Oxford University Press.
    • (1996) Bayesian Statistics , vol.5
    • Gelman, A.1    Roberts, R.O.2    Gilks, W.R.3
  • 11
  • 13
    • 84950437936 scopus 로고
    • Annealing Markov Chain Monte Carlo With Applications to Pedigree Analysis
    • Geyer, C. J., and Thompson, E. A. (1995), “Annealing Markov Chain Monte Carlo With Applications to Pedigree Analysis,” Journal of American Statistical Association, 90, 909–920.
    • (1995) Journal of American Statistical Association , vol.90 , pp. 909-920
    • Geyer, C.J.1    Thompson, E.A.2
  • 16
    • 77956889087 scopus 로고
    • Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination
    • Green, P. J. (1995), “Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination,” Biometrika, 82, 711–732.
    • (1995) Biometrika , vol.82 , pp. 711-732
    • Green, P.J.1
  • 17
    • 77956890234 scopus 로고
    • Monte Carlo Sampling Methods Using Markov Chain and Their Applications
    • Hastings, W. K. (1970), “Monte Carlo Sampling Methods Using Markov Chain and Their Applications,” Biometrika, 57, 97–109.
    • (1970) Biometrika , vol.57 , pp. 97-109
    • Hastings, W.K.1
  • 18
    • 0000860595 scopus 로고    scopus 로고
    • Neural Network Models for Time Series Forecasts
    • Hill, T., O’Connor, M., and Remus, W. (1996), “Neural Network Models for Time Series Forecasts,” Management Science, 42, 1082–1092.
    • (1996) Management Science , vol.42 , pp. 1082-1092
    • Hill, T.1    O’Connor, M.2    Remus, W.3
  • 20
    • 0024880831 scopus 로고
    • Multilayer Feedforward Networks are Universal Approximators
    • Hornik, K., Stinchcombe, M., and White, H. (1989), “Multilayer Feedforward Networks are Universal Approximators,” Neural Networks, 2, 359–366.
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 21
    • 0030516672 scopus 로고    scopus 로고
    • Exchange Monte Carlo Method and Application to Spin Glass Simulations
    • Hukushima, K., and Nemoto, K. (1996), “Exchange Monte Carlo Method and Application to Spin Glass Simulations,” Journal of the Physics Society of Japan, 65, 1604–1608.
    • (1996) Journal of the Physics Society of Japan , vol.65 , pp. 1604-1608
    • Hukushima, K.1    Nemoto, K.2
  • 23
    • 0043283343 scopus 로고    scopus 로고
    • Dynamic Weighting in Simulations of Spin Systems
    • Ser. A
    • Liang, F., and Wong, W. H. (1999), “Dynamic Weighting in Simulations of Spin Systems,” Physics Letters, Ser. A, 252, 257–262.
    • (1999) Physics Letters , vol.252 , pp. 257-262
    • Liang, F.1    Wong, W.H.2
  • 24
    • 0034403006 scopus 로고    scopus 로고
    • Evolutionary Monte Carlo Sampling: Applications to Cp Model Sampling and Change-point Problem
    • Liang, F., and Wong, W. H. (2000), “Evolutionary Monte Carlo Sampling: Applications to Cp Model Sampling and Change-point Problem,” Statistica Sinica, 10, 317–342.
    • (2000) Statistica Sinica , vol.10 , pp. 317-342
    • Liang, F.1    Wong, W.H.2
  • 25
    • 0442309554 scopus 로고    scopus 로고
    • The Use of Multiple-Try Method and Local Optimization in Metropolis Sampling
    • Liu, J. S., Liang F., and Wong, W. H. (2000), “The Use of Multiple-Try Method and Local Optimization in Metropolis Sampling,” Journal of American Statistical Association, 94, 121–134.
    • (2000) Journal of American Statistical Association , vol.94 , pp. 121-134
    • Liu, J.S.1    Liang, F.2    Wong, W.H.3
  • 26
    • 0002704818 scopus 로고
    • A Practical Bayesian Framework for Backprop Networks
    • MacKay, D. J. C. (1992), “A Practical Bayesian Framework for Backprop Networks,” Neural Computation, 4, 448–472.
    • (1992) Neural Computation , vol.4 , pp. 448-472
    • Mackay, D.J.C.1
  • 27
    • 33644899039 scopus 로고
    • Simulated Tempering: A New Monte Carlo Scheme
    • Marinari, E., and Parisi, G. (1992), “Simulated Tempering: A New Monte Carlo Scheme,” Europhysics Letters, 19, 451–458.
    • (1992) Europhysics Letters , vol.19 , pp. 451-458
    • Marinari, E.1    Parisi, G.2
  • 29
    • 21444451325 scopus 로고    scopus 로고
    • Simulating Ratios of Normalizing Constants via a Simple Identity: A Theoretical Exploration
    • Meng, X., and Wong, W. H. (1996), “Simulating Ratios of Normalizing Constants via a Simple Identity: A Theoretical Exploration,” Statistica Sinica, 6, 831–860.
    • (1996) Statistica Sinica , vol.6 , pp. 831-860
    • Meng, X.1    Wong, W.H.2
  • 31
    • 0347128520 scopus 로고    scopus 로고
    • Issues in Bayesian Analysis of Neural Network Models
    • Müller, P., and Insua, D. R. (1998), “Issues in Bayesian Analysis of Neural Network Models,” Neural Computation, 10, 749–770.
    • (1998) Neural Computation , vol.10 , pp. 749-770
    • Müller, P.1    Insua, D.R.2
  • 32
    • 0037591475 scopus 로고
    • Bayesian Learning via Stochastic Dynamics
    • C. L. Giles, S. J. Hansn, and J. D. Cowan, San Francisco: Morgan Kaufmann
    • Neal, R. M. (1993), “Bayesian Learning via Stochastic Dynamics,” in Advances in Neural Information Processing Systems 5, eds. C. L. Giles, S. J. Hansn, and J. D. Cowan, San Francisco: Morgan Kaufmann.
    • (1993) Advances in Neural Information Processing Systems , vol.5
    • Neal, R.M.1
  • 35
    • 0003066726 scopus 로고    scopus 로고
    • A Real-Coded Genetic Algorithm for Function Optimization Using Unimodal Normal Distribution Crossover
    • T. Bäck, San Francisco: Morgan Kaufmann
    • Ono, I., and Kobayashi, S. (1997), “A Real-Coded Genetic Algorithm for Function Optimization Using Unimodal Normal Distribution Crossover,” in Proceedings of the Seventh International Conference on Genetic Algorithms, ed. T. Bäck, San Francisco: Morgan Kaufmann, pp. 246–253.
    • (1997) Proceedings of the Seventh International Conference on Genetic Algorithms , pp. 246-253
    • Ono, I.1    Kobayashi, S.2
  • 36
    • 0002013327 scopus 로고
    • Bayesian Model Comparison via Jump Diffusions
    • W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, London: Chapman and Hall
    • Phillips, D. B., and Smith, A. F. M. (1995), “Bayesian Model Comparison via Jump Diffusions,” in Markov Chain Monte Carlo in Practice, eds. W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, London: Chapman and Hall, pp. 215–239.
    • (1995) Markov Chain Monte Carlo in Practice , pp. 215-239
    • Phillips, D.B.1    Smith, A.F.M.2
  • 37
    • 0001180165 scopus 로고
    • Probes of Large-Scale Structures in the Corona Borealis Region
    • Postman, M., Huchra, J. P., and Geller, M. J. (1986), “Probes of Large-Scale Structures in the Corona Borealis Region,” The Astronomical Journal, 92, 1238–1247.
    • (1986) The Astronomical Journal , vol.92 , pp. 1238-1247
    • Postman, M.1    Huchra, J.P.2    Geller, M.J.3
  • 38
    • 0000940729 scopus 로고
    • Facilitating the Gibbs Sampler: The Gibbs Stopper and the Griddy-Gibbs Sampler
    • 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.
    • (1992) Journal of the American Statistical Association , vol.87 , pp. 861-868
    • Ritter, C.1    Tanner, M.A.2
  • 39
  • 40
    • 0000795635 scopus 로고
    • Density Estimation With Confidence Sets Exemplified by Superclusters and Voids in Galaxies
    • Roeder, K. (1990), “Density Estimation With Confidence Sets Exemplified by Superclusters and Voids in Galaxies,” Journal of the American Statistical Association, 85, 617–624.
    • (1990) Journal of the American Statistical Association , vol.85 , pp. 617-624
    • Roeder, K.1
  • 41
    • 0000646059 scopus 로고
    • Learning Internal Representations by Error Propagation
    • D. Rumelhart and J. McClelland, Cambridge, MA: MIT Press
    • Rumelhart, D., Hinton, G., and McClelland, J. (1986), “Learning Internal Representations by Error Propagation,” in Parallel Distributed Processing, eds. D. Rumelhart and J. McClelland, Cambridge, MA: MIT Press, pp. 45–76.
    • (1986) Parallel Distributed Processing , pp. 45-76
    • Rumelhart, D.1    Hinton, G.2    McClelland, J.3
  • 44
    • 0001403575 scopus 로고
    • Genetic Algorithms for Real Parameter Optimization
    • G. J. E. Rawlins, San Mateo, CA: Morgan Kaufmann
    • Wright, A. H. (1991), “Genetic Algorithms for Real Parameter Optimization,” in Foundations of Genetic Algorithms 1, ed. G. J. E. Rawlins, San Mateo, CA: Morgan Kaufmann, pp. 205–218.
    • (1991) Foundations of Genetic Algorithms , vol.1 , pp. 205-218
    • Wright, A.H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.