메뉴 건너뛰기




Volumn 93, Issue 2, 2006, Pages 451-458

Miscellanea an efficient Markov chain Monte Carlo method for distributions with intractable normalising constants

Author keywords

Auxiliary variable method; Ising model; Markov chain Monte Carlo; Metropolis Hastings algorithm; Normalising constant; Partition function

Indexed keywords


EID: 33745612435     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/93.2.451     Document Type: Article
Times cited : (323)

References (22)
  • 1
    • 6944224314 scopus 로고    scopus 로고
    • A recursive algorithm for Markov random fields
    • BARTOLUCCI, F. & BESAG, J. (2002). A recursive algorithm for Markov random fields. Biometrika 89, 724-30.
    • (2002) Biometrika , vol.89 , pp. 724-730
    • Bartolucci, F.1    Besag, J.2
  • 2
    • 0041360504 scopus 로고    scopus 로고
    • Likelihood and non-parametric Bayesian MCMC inference for spatial point processes based on perfect simulation and path sampling
    • BERTHELSEN, K. K. & MØLLER, J. (2003). Likelihood and non-parametric Bayesian MCMC inference for spatial point processes based on perfect simulation and path sampling. Scand. J. Statist. 30, 549-64.
    • (2003) Scand. J. Statist. , vol.30 , pp. 549-564
    • Berthelsen, K.K.1    Møller, J.2
  • 3
    • 33745607179 scopus 로고    scopus 로고
    • Bayesian analysis of Markov point processes
    • Ed. A. Baddeley, P. Gregori, J. Mateu, R. Stoica and D. Stoyan, New York: Springer
    • BERTHELSEN, K. K. & MØLLER, J. (2006). Bayesian analysis of Markov point processes. In Case Studies in Spatial Point Process Modeling, Ed. A. Baddeley, P. Gregori, J. Mateu, R. Stoica and D. Stoyan, pp. 85-97, New York: Springer.
    • (2006) Case Studies in Spatial Point Process Modeling , pp. 85-97
    • Berthelsen, K.K.1    Møller, J.2
  • 4
    • 0000582521 scopus 로고
    • Statistical analysis of non-lattice data
    • BESAG, J. (1975), Statistical analysis of non-lattice data. The Statistician 24, 179-95.
    • (1975) The Statistician , vol.24 , pp. 179-195
    • Besag, J.1
  • 5
    • 0000913755 scopus 로고
    • Spatial interaction and the statistical analysis of lattice systems
    • BESAG, J. E. (1974). Spatial interaction and the statistical analysis of lattice systems (with Discussion). J. R. Statist. Soc. B 36, 192-236.
    • (1974) J. R. Statist. Soc. B , vol.36 , pp. 192-236
    • Besag, J.E.1
  • 6
    • 0041906743 scopus 로고    scopus 로고
    • From Metropolis to diffusions: Gibbs states and optimal scaling
    • BREYER, L. A. & ROBERTS, G. O. (2000). From Metropolis to diffusions: Gibbs states and optimal scaling. Stoch. Proces. Applic. 90, 181-206.
    • (2000) Stoch. Proces. Applic. , vol.90 , pp. 181-206
    • Breyer, L.A.1    Roberts, G.O.2
  • 7
    • 0031527297 scopus 로고    scopus 로고
    • On Monte Carlo methods for estimating ratios of normalizing constants
    • CHEN, M.-H. & SHAO, Q.-M. (1997). On Monte Carlo methods for estimating ratios of normalizing constants. Ann. Statist. 25, 1563-94.
    • (1997) Ann. Statist. , vol.25 , pp. 1563-1594
    • Chen, M.-H.1    Shao, Q.-M.2
  • 8
    • 33745628537 scopus 로고    scopus 로고
    • Directed Markov point processes as limits of partially ordered Markov models
    • CRESSIE, N., ZHU, J., BADDELEY, A. J. & NAIR, M. G. (2000). Directed Markov point processes as limits of partially ordered Markov models. Methodol. Comp. Appl. Prob. 2, 5-21.
    • (2000) Methodol. Comp. Appl. Prob. , vol.2 , pp. 5-21
    • Cressie, N.1    Zhu, J.2    Baddeley, A.J.3    Nair, M.G.4
  • 9
    • 0000736067 scopus 로고    scopus 로고
    • Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
    • GELMAN, A. & MENG, X.-L. (1998). Simulating normalizing constants: from importance sampling to bridge sampling to path sampling. Statist. Sci. 13, 163-85.
    • (1998) Statist. Sci. , vol.13 , pp. 163-185
    • Gelman, A.1    Meng, X.-L.2
  • 11
    • 0000051109 scopus 로고
    • Constrained Monte Carlo maximum likelihood for dependent data
    • GEYER, C. J. & THOMPSON, E. A. (1992). Constrained Monte Carlo maximum likelihood for dependent data (with Discussion). J. R. Statist. Soc. B 54, 657-99.
    • (1992) J. R. Statist. Soc. B , vol.54 , pp. 657-699
    • Geyer, C.J.1    Thompson, E.A.2
  • 12
    • 0036970574 scopus 로고    scopus 로고
    • Hidden Markov models and disease mapping
    • GREEN, P. J. & RICHARDSON, S. (2002). Hidden Markov models and disease mapping. J. Am. Statist. Assoc. 97, 1055-70.
    • (2002) J. Am. Statist. Assoc. , vol.97 , pp. 1055-1070
    • Green, P.J.1    Richardson, S.2
  • 13
    • 0041339533 scopus 로고    scopus 로고
    • Parametric estimation in Markov random field image modeling with imperfect observations. A comparative study
    • IBANEZ, M. & SIMO, A. (2003). Parametric estimation in Markov random field image modeling with imperfect observations. A comparative study. Pat. Recog. Lett. 24, 2377-89.
    • (2003) Pat. Recog. Lett. , vol.24 , pp. 2377-2389
    • Ibanez, M.1    Simo, A.2
  • 14
    • 0036004159 scopus 로고    scopus 로고
    • Difficulties in estimating the normalizing constant of the posterior for a neural network
    • LEE, H. K. H. (2002). Difficulties in estimating the normalizing constant of the posterior for a neural network. J. Comp. Graph. Statist. 11, 222-35.
    • (2002) J. Comp. Graph. Statist. , vol.11 , pp. 222-235
    • Lee, H.K.H.1
  • 15
    • 21444451325 scopus 로고    scopus 로고
    • Simulating ratios of normalizing constants via a simple identity: A theoretical exploration
    • MENG, X. L. & WONG, W. H. (1996). Simulating ratios of normalizing constants via a simple identity: a theoretical exploration. Statist. Sinica 6, 831-60.
    • (1996) Statist. Sinica , vol.6 , pp. 831-860
    • Meng, X.L.1    Wong, W.H.2
  • 17
    • 0005193926 scopus 로고    scopus 로고
    • Exact sampling with coupled Markov chains and applications to statistical mechanics
    • PROPP, J. & WILSON, D. (1996). Exact sampling with coupled Markov chains and applications to statistical mechanics. Random Struct. Algor. 9, 223-52.
    • (1996) Random Struct. Algor. , vol.9 , pp. 223-252
    • Propp, J.1    Wilson, D.2
  • 18
    • 33745604624 scopus 로고    scopus 로고
    • A theoretical framework for approximate Bayesian computation
    • Ed. A. Francis, K. Matawie, A. Oshlack and G. Smyth, Sydney, Australia: University of Western Sydney
    • REEVES, R. & PETTITT, A. (2005). A theoretical framework for approximate Bayesian computation. In 20th International Workshop on Statistical Modelling, Ed. A. Francis, K. Matawie, A. Oshlack and G. Smyth, pp. 393-6. Sydney, Australia: University of Western Sydney.
    • (2005) 20th International Workshop on Statistical Modelling , pp. 393-396
    • Reeves, R.1    Pettitt, A.2
  • 19
    • 24944590473 scopus 로고    scopus 로고
    • Efficient recursions for general factorisable models
    • REEVES, R. & PETTITT, A. N. (2004). Efficient recursions for general factorisable models. Biometrika 91, 751-7.
    • (2004) Biometrika , vol.91 , pp. 751-757
    • Reeves, R.1    Pettitt, A.N.2
  • 21
    • 0035995077 scopus 로고    scopus 로고
    • Hyper inverse Wishart distribution for non-decomposable graphs and its application to Bayesian inference for Gaussian graphical models
    • ROVERATO, A. (2002). Hyper inverse Wishart distribution for non-decomposable graphs and its application to Bayesian inference for Gaussian graphical models. Scand. J. Statist. 29, 391-411.
    • (2002) Scand. J. Statist. , vol.29 , pp. 391-411
    • Roverato, A.1
  • 22
    • 3843149220 scopus 로고    scopus 로고
    • Efficient estimation of covariance selection models
    • WONG, F., CARTER, C. K. & KOHN, R. (2003). Efficient estimation of covariance selection models. Biometrika 90, 809-30.
    • (2003) Biometrika , vol.90 , pp. 809-830
    • Wong, F.1    Carter, C.K.2    Kohn, R.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.