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Volumn 16, Issue 2, 2006, Pages 233-240

Node assignment problem in Bayesian networks

Author keywords

Bayesian networks; Biostatistics; Confidence intervals; Maximum likelihood

Indexed keywords

MAXIMUM LIKELIHOOD ESTIMATION; OPTIMIZATION; PROBABILITY DISTRIBUTIONS; STATISTICAL METHODS; TOPOLOGY;

EID: 33745481882     PISSN: 1641876X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (3)

References (23)
  • 1
    • 0026374073 scopus 로고
    • Bayesian networks without tears
    • Charniak E. (1991): Bayesian networks without tears. - AI Magazine, Vol. 12, No. 4, pp. 50-63.
    • (1991) AI Magazine , vol.12 , Issue.4 , pp. 50-63
    • Charniak, E.1
  • 2
    • 0042496103 scopus 로고    scopus 로고
    • Learning equivalence classes of Bayesian-network structures
    • Chickering D.M. (2002): Learning equivalence classes of Bayesian-network structures. - J. Mach. Learn. Res., Vol. 2, No. 3, pp. 445-498.
    • (2002) J. Mach. Learn. Res. , vol.2 , Issue.3 , pp. 445-498
    • Chickering, D.M.1
  • 5
    • 0842288337 scopus 로고    scopus 로고
    • Inferring cellular networks using probabilistic graphical models
    • Friedman N. (2004).: Inferring cellular networks using probabilistic graphical models. - Science, Vol. 303, No. 5659, pp. 799-805.
    • (2004) Science , vol.303 , Issue.5659 , pp. 799-805
    • Friedman, N.1
  • 6
    • 0033707946 scopus 로고    scopus 로고
    • Using Bayesian networks to analyze expression data
    • Friedman N., Linial M., Nachman I. and Pe'er D. (2000): Using Bayesian networks to analyze expression data. - J. Comput. Biol., Vol. 7, Nos. 3-4, pp. 601-620.
    • (2000) J. Comput. Biol. , vol.7 , Issue.3-4 , pp. 601-620
    • Friedman, N.1    Linial, M.2    Nachman, I.3    Pe'er, D.4
  • 7
    • 0037379940 scopus 로고    scopus 로고
    • Cause-effect relationships in analytical surveys: An illustration of statistical issues
    • Gadbury G.L. and Schreuder H.T. (2003): Cause-effect relationships in analytical surveys: An illustration of statistical issues. - Env. Monit. Assess., Vol. 83, No. 3, pp. 205-227.
    • (2003) Env. Monit. Assess. , vol.83 , Issue.3 , pp. 205-227
    • Gadbury, G.L.1    Schreuder, H.T.2
  • 9
    • 0003846041 scopus 로고
    • A tutorial on learning with Bayesian networks
    • Tech. Rep., MSR-TR-95-06, available at: ftp://ftp.research.microsoft.com/pub/tr/tr-95-06.pdf
    • Heckerman D. (1995): A tutorial on learning with Bayesian networks. - Tech. Rep., MSR-TR-95-06, available at: Ftp://ftp.research.microsoft.com/ pub/tr/tr-95-06.pdf
    • (1995)
    • Heckerman, D.1
  • 11
    • 0000801240 scopus 로고    scopus 로고
    • Discovering regulatory and signaling circuits in molecular interaction networks
    • 90001
    • Ideker T., Ozier O., Schwikowski B. and Siegel A.F. (2002): Discovering regulatory and signaling circuits in molecular interaction networks. - Bioinf. Vol. 18, Suppl. 1, No. 90001, pp. S233-S240.
    • (2002) Bioinf. , vol.18 , Issue.SUPPL. 1
    • Ideker, T.1    Ozier, O.2    Schwikowski, B.3    Siegel, A.F.4
  • 14
    • 0003694781 scopus 로고    scopus 로고
    • Bayes net toolbox for matlab
    • Available at
    • Murphy K. (2005): Bayes net toolbox for Hatlab. - Available at: http:// bnt.sourceforge.net/
    • (2005)
    • Murphy, K.1
  • 15
    • 0031273002 scopus 로고    scopus 로고
    • A method of learning implication networks from empirical data: Algorithm and Monte-Carlo simulation-based validation
    • Liu J. and Desmarais M.C. (1997): A method of learning implication networks from empirical data: Algorithm and Monte-Carlo simulation-based validation. - IEEE Trans. Knowl. Data Eng., Vol. 9, No. 6, pp. 990-1004.
    • (1997) IEEE Trans. Knowl. Data Eng. , vol.9 , Issue.6 , pp. 990-1004
    • Liu, J.1    Desmarais, M.C.2
  • 19
    • 0002838962 scopus 로고
    • A theory of inferred causation
    • (J.A. Allen, R. Fikes and E. Sandewall, Eds.). - San Mateo: Morgan Kaufmann
    • Pearl J. and Verma T.S. (1991): A theory of inferred causation, In: Principles of Knowledge Representation and Reasoning, (J.A. Allen, R. Fikes and E. Sandewall, Eds.). - San Mateo: Morgan Kaufmann.
    • (1991) Principles of Knowledge Representation and Reasoning
    • Pearl, J.1    Verma, T.S.2
  • 20
    • 18144442687 scopus 로고    scopus 로고
    • Inferring subnetworks from perturbed expression profiles
    • 90001
    • Pe'er D., Regev A., Elidan G. and Friedman N. (2001): Inferring subnetworks from perturbed expression profiles. - Bioinf., Vol. 17, Suppl. 1, No. 90001, pp. S215-S224.
    • (2001) Bioinf. , vol.17 , Issue.SUPPL. 1
    • Pe'er, D.1    Regev, A.2    Elidan, G.3    Friedman, N.4
  • 21
    • 33745506604 scopus 로고    scopus 로고
    • Inferring cause - Effect relations from gene expression profiles of cancer versus normal cells
    • Tech. Rep., available at
    • Polanski A., Polanska J., Jarzab M., Wiench M. and Jarzab B., (2005): Inferring cause - effect relations from gene expression profiles of cancer versus normal cells. - Tech. Rep., available at: http:// web.zis.ia.polsl.gliwice.pl/publikacje/projekty/technical_report.pdf
    • (2005)
    • Polanski, A.1    Polanska, J.2    Jarzab, M.3    Wiench, M.4    Jarzab, B.5
  • 23
    • 0035237805 scopus 로고    scopus 로고
    • Rich probabilistic models for gene expression
    • Segal E., Taskar B., Gasch A., Friedman N. and Koller D. (2001): Rich probabilistic models for gene expression. - Bioinf, Vol. 1, No. 1, pp. 1-10.
    • (2001) Bioinf. , vol.1 , Issue.1 , pp. 1-10
    • Segal, E.1    Taskar, B.2    Gasch, A.3    Friedman, N.4    Koller, D.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.