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Volumn 5, Issue , 2009, Pages 504-511

Locally minimax optimal predictive modeling with Bayesian networks

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN APPROACHES; BAYESIAN NETWORK MODELS; EMPIRICAL TEST; HYPERPARAMETERS; INFORMATION-THEORETIC APPROACH; MARGINAL LIKELIHOOD; MDL PRINCIPLE; MINIMAX; MODEL SELECTION CRITERIA; NORMALIZED MAXIMUM LIKELIHOOD; PARAMETER LEARNING; PREDICTIVE MODELING;

EID: 84862284053     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (8)

References (16)
  • 1
    • 0000501656 scopus 로고
    • Information theory and an extension of the maximum likelihood principle
    • B. Petrox & F. Caski (Eds.), Budapest: Akademiai Kiado
    • Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. Petrox & F. Caski (Eds.), Proceedings of the second international symposium on information theory (pp. 267-281). Budapest: Akademiai Kiado.
    • (1973) Proceedings of the Second International Symposium on Information Theory , pp. 267-281
    • Akaike, H.1
  • 2
    • 0001019707 scopus 로고    scopus 로고
    • Learning bayesian networks is NP-Complete
    • D. Fisher & H. Lenz (Eds.), Springer-Verlag
    • Chickering, D. (1996). Learning Bayesian networks is NP-Complete. In D. Fisher & H. Lenz (Eds.), Learning from data: Artificial intelligence and statistics v (pp. 121-130). Springer-Verlag.
    • (1996) Learning from Data: Artificial Intelligence and Statistics V , pp. 121-130
    • Chickering, D.1
  • 3
  • 4
    • 34249761849 scopus 로고
    • Learning bayesian networks: The combination of knowledge and statistical data
    • Heckerman, D., Geiger, D., & Chickering, D. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20 (3), 197-243.
    • (1995) Machine Learning , vol.20 , Issue.3 , pp. 197-243
    • Heckerman, D.1    Geiger, D.2    Chickering, D.3
  • 5
    • 34347261665 scopus 로고    scopus 로고
    • A lineartime algorithm for computing the multinomial stochastic complexity
    • Kontkanen, P., & Myllym̈aki, P. (2007). A lineartime algorithm for computing the multinomial stochastic complexity. Information Processing Letters, 103 (6), 227-233.
    • (2007) Information Processing Letters , vol.103 , Issue.6 , pp. 227-233
    • Kontkanen, P.1    Myllym̈aki, P.2
  • 7
    • 0029777083 scopus 로고    scopus 로고
    • Fisher information and stochastic complexity
    • Rissanen, J. (1996). Fisher information and stochastic complexity. IEEE Transactions on Information Theory, 42 (1), 40-47.
    • (1996) IEEE Transactions on Information Theory , vol.42 , Issue.1 , pp. 40-47
    • Rissanen, J.1
  • 10
    • 0000120766 scopus 로고
    • Estimating the dimension of a model
    • Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
    • (1978) Annals of Statistics , vol.6 , pp. 461-464
    • Schwarz, G.1
  • 11
    • 0023380770 scopus 로고
    • Universal sequential coding of single messages
    • Shtarkov, Y. (1987). Universal sequential coding of single messages. Problems of Information Transmission, 23, 3-17.
    • (1987) Problems of Information Transmission , vol.23 , pp. 3-17
    • Shtarkov, Y.1
  • 13
    • 80053201441 scopus 로고    scopus 로고
    • A simple approach for finding the globally optimal Bayesian network structure
    • R. Dechter & T. Richardson (Eds.), AUAI Press
    • Silander, T., & Myllym̈aki, P. (2006). A simple approach for finding the globally optimal Bayesian network structure. In R. Dechter & T. Richardson (Eds.), Proceedings of the 22nd conference on uncertainty in artificial intelligence (pp. 445- 452). AUAI Press.
    • (2006) Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence , pp. 445-452
    • Silander, T.1    Myllym̈aki, P.2
  • 15
    • 85156264409 scopus 로고    scopus 로고
    • On the dirichlet prior and bayesian regularization
    • Vancouver, Canada: MIT Press
    • Steck, H., & Jaakkola, T. S. (2002). On the Dirichlet prior and Bayesian regularization. In Advances in neural information processing systems 15 (pp. 697-704). Vancouver, Canada: MIT Press.
    • (2002) Advances in Neural Information Processing Systems , vol.15 , pp. 697-704
    • Steck, H.1    Jaakkola, T.S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.