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Volumn 25, Issue 11, 1996, Pages 2493-2519

Bayesian model averaging and model selection for Markov equivalence classes of acyclic digraphs

Author keywords

Bayesian graphical model; Essential graph; Markov chain Monte Carlo; Markov equivalence; Model averaging; Model uncertainty

Indexed keywords


EID: 0000220791     PISSN: 03610926     EISSN: None     Source Type: Journal    
DOI: 10.1080/03610929608831853     Document Type: Article
Times cited : (95)

References (13)
  • 1
    • 0012812620 scopus 로고
    • STENO: An expert system for medical diagnosis based on graphical models and model search
    • Andersen, L.R., Krebs, J.H., and Damgaard, J. (1991) STENO: an expert system for medical diagnosis based on graphical models and model search. Journal of Applied Statistics, 18, 139-153.
    • (1991) Journal of Applied Statistics , vol.18 , pp. 139-153
    • Andersen, L.R.1    Krebs, J.H.2    Damgaard, J.3
  • 2
    • 0040388111 scopus 로고
    • On the Markov equivalence of chain graphs, undirected graphs, and acyclic digraphs
    • to appear
    • Andersson, S. A., D. Madigan, and M. D. Perlman (1995a). On the Markov equivalence of chain graphs, undirected graphs, and acyclic digraphs. Scandinavian Journal of Statistics, to appear.
    • (1995) Scandinavian Journal of Statistics
    • Andersson, S.A.1    Madigan, D.2    Perlman, M.D.3
  • 7
    • 0002013121 scopus 로고
    • A transformational characterization of equivalent Bayesian network structures
    • (P.Besnard and S. Hanks, eds.), San Francisco: Morgan Kaufman
    • Chickering, D. M. (1995). A transformational characterization of equivalent Bayesian network structures. In Uncertainty in Artificial Intelligence, Proceedings of the Eleventh Conference (P.Besnard and S. Hanks, eds.), San Francisco: Morgan Kaufman, pp. 87-98.
    • (1995) Uncertainty in Artificial Intelligence, Proceedings of the Eleventh Conference , pp. 87-98
    • Chickering, D.M.1
  • 8
    • 34249832377 scopus 로고
    • A Bayesian method for the induction of probabilistic networks from data
    • Cooper, G. F. and E. Herskovits (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309-347.
    • (1992) Machine Learning , vol.9 , pp. 309-347
    • Cooper, G.F.1    Herskovits, E.2
  • 9
    • 21344482755 scopus 로고
    • Hyper Markov laws in the statistical analysis of decomposable graphical models
    • Dawid, A. P. and S. L. Lauritzen (1993). Hyper Markov laws in the statistical analysis of decomposable graphical models. Annals of Statistics 21, 1272-1317.
    • (1993) Annals of Statistics , vol.21 , pp. 1272-1317
    • Dawid, A.P.1    Lauritzen, S.L.2
  • 10
    • 0000130823 scopus 로고
    • A fast procedure for model search in multidimensional contingency tables
    • Edwards, D. and T. Havránek (1985). A fast procedure for model search in multidimensional contingency tables. Biometrika 72, 339-351.
    • (1985) Biometrika , vol.72 , pp. 339-351
    • Edwards, D.1    Havránek, T.2
  • 13
    • 0002977294 scopus 로고
    • A characterization of the Dirichlet distribution with applications to learning Bayesian networks
    • (P.Besnard and S. Hanks, eds.), San Francisco: Morgan Kaufman
    • Geiger, D. and D. Heckerman (1995). A characterization of the Dirichlet distribution with applications to learning Bayesian networks. In Uncertainty in Artificial Intelligence, Proceedings of the Eleventh Conference (P.Besnard and S. Hanks, eds.), San Francisco: Morgan Kaufman, pp. 196-207.
    • (1995) Uncertainty in Artificial Intelligence, Proceedings of the Eleventh Conference , pp. 196-207
    • Geiger, D.1    Heckerman, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.