메뉴 건너뛰기




Volumn 41, Issue 16-17, 2012, Pages 3233-3243

Bayesian network structure learning with permutation tests

Author keywords

Bayesian networks; Conditional independence tests; Permutation tests; Shrinkage tests

Indexed keywords

BAYESIAN NETWORK STRUCTURE; CONDITIONAL INDEPENDENCES; CONSTRAINT-BASED; DISCRETE DATA; MAXIMIZATION ALGORITHM; PERMUTATION TESTS; SHRINKAGE TEST;

EID: 84866045800     PISSN: 03610926     EISSN: 1532415X     Source Type: Journal    
DOI: 10.1080/03610926.2011.593284     Document Type: Article
Times cited : (19)

References (26)
  • 2
    • 0002460150 scopus 로고
    • The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks
    • New York: Springer-Verlag
    • Beinlich, I., Suermondt, H. J., Chavez, R. M., Cooper, G. F. (1989). The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. Proc. 2nd Eur. Conf. Artif. Intell. Med. New York: Springer-Verlag, pp. 247-256.
    • (1989) Proc. 2nd Eur. Conf. Artif. Intell. Med , pp. 247-256
    • Beinlich, I.1    Suermondt, H.J.2    Chavez, R.M.3    Cooper, G.F.4
  • 4
    • 77957967782 scopus 로고    scopus 로고
    • Improving Bayesian network parameter learning using constraints
    • New York: IEEE Press
    • de Campos, C. P., Ji, Q. (2008). Improving Bayesian network parameter learning using constraints. Proc. 19th Int. Conf. Patt. Recogn. (ICPR '08), New York: IEEE Press, pp. 1-4.
    • (2008) Proc. 19th Int. Conf. Patt. Recogn. (ICPR '08) , pp. 1-4
    • De Campos, C.P.1    Ji, Q.2
  • 6
    • 0004675530 scopus 로고
    • Learning Gaussian networks
    • Redmond, WA. Available as Technical Report MSR-TR-94-10
    • Geiger, D., Heckerman, D. (1994). Learning Gaussian networks. Technical report, Microsoft Research, Redmond, WA. Available as Technical Report MSR-TR-94-10.
    • (1994) Technical Report, Microsoft Research
    • Geiger, D.1    Heckerman, D.2
  • 7
    • 68949161935 scopus 로고    scopus 로고
    • Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks
    • Hausser, J., Strimmer, K. (2009). Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks. J. Mach. Learn. Res. 10:1469-1484.
    • (2009) J. Mach. Learn. Res. , vol.10 , pp. 1469-1484
    • Hausser, J.1    Strimmer, K.2
  • 8
    • 34249761849 scopus 로고
    • Learning Bayesian networks: The combination of knowledge and statistical data
    • Available as Technical Report MSR-TR-94-09
    • Heckerman, D., Geiger, D., Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Mach. Learn. 20(3):197-243. Available as Technical Report MSR-TR-94-09.
    • (1995) Mach. Learn. , vol.20 , Issue.3 , pp. 197-243
    • Heckerman, D.1    Geiger, D.2    Chickering, D.M.3
  • 9
    • 0001486499 scopus 로고
    • Estimation with quadratic loss
    • Neyman, J., Ed. Berkeley, CA: University of California Press
    • James, W., Stein, C. (1961). Estimation with quadratic loss. In: Neyman, J., Ed. Proc. 4th Berkeley Symp. Mathemat. Statist. Probab. Berkeley, CA: University of California Press, pp. 361-379.
    • (1961) Proc. 4th Berkeley Symp. Mathemat. Statist. Probab. , pp. 361-379
    • James, W.1    Stein, C.2
  • 12
    • 73149097219 scopus 로고    scopus 로고
    • Regularized estimation of large-scale gene association networks using graphical gaussian models
    • Krämer, N., Schäfer, J., Boulesteix, A. (2009). Regularized estimation of large-scale gene association networks using graphical gaussian models. BMC Bioinform. 10(1):384.
    • (2009) BMC Bioinform , vol.10 , Issue.1 , pp. 384
    • Krämer, N.1    Schäfer, J.2    Boulesteix, A.3
  • 14
  • 15
    • 0041841552 scopus 로고    scopus 로고
    • Improved estimation of the covariance matrix of stock returns with an application to portfolio selection
    • Ledoit, O., Wolf, M. (2003). Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J. Empir. Fina. 10:603-621.
    • (2003) J. Empir. Fina. , vol.10 , pp. 603-621
    • Ledoit, O.1    Wolf, M.2
  • 17
    • 84950435288 scopus 로고
    • A network algorithm for performing fisher's exact test in r × c contingency tables
    • Mehta, C., Patel, N. (1983). A network algorithm for performing fisher's exact test in r × c contingency tables. J. Amer. Statist. Assoc. 78:427-434.
    • (1983) J. Amer. Statist. Assoc. , vol.78 , pp. 427-434
    • Mehta, C.1    Patel, N.2
  • 18
    • 1942452317 scopus 로고    scopus 로고
    • Optimal reinsertion: A new search operator for accelerated and more accurate Bayesian network structure learning
    • New York: AAAI Press
    • Moore, A., Wong, W. (2003). Optimal reinsertion: A new search operator for accelerated and more accurate Bayesian network structure learning. Proc. 20th Int. Conf. Mach. Learn. (ICML '03), New York: AAAI Press, pp. 552-559.
    • (2003) Proc. 20th Int. Conf. Mach. Learn. (ICML '03) , pp. 552-559
    • Moore, A.1    Wong, W.2
  • 21
    • 79951480123 scopus 로고    scopus 로고
    • R Development Core Team. Vienna: R Foundation for Statistical Computing
    • R Development Core Team (2010). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.
    • (2010) R: A Language and Environment for Statistical Computing
  • 22
    • 27844521293 scopus 로고    scopus 로고
    • A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics
    • Schäfer, J., Strimmer, K. (2005). A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statist. Applic. Genet. Molec. Biol. 4:32.
    • (2005) Statist. Applic. Genet. Molec. Biol. , vol.4 , pp. 32
    • Schäfer, J.1    Strimmer, K.2
  • 23
    • 0000120766 scopus 로고
    • Estimating the dimension of a model
    • Schwarz, G. E. (1978). Estimating the dimension of a model. Ann. Statist. 6(2):461-464.
    • (1978) Ann. Statist. , vol.6 , Issue.2 , pp. 461-464
    • Schwarz, G.E.1
  • 24
    • 77955124773 scopus 로고    scopus 로고
    • Learning Bayesian networks with the bnlearn R package
    • Scutari, M. (2010). Learning Bayesian networks with the bnlearn R package. J. Statist. Software 35(3):1-22.
    • (2010) J. Statist. Software , vol.35 , Issue.3 , pp. 1-22
    • Scutari, M.1
  • 25
    • 0000813561 scopus 로고
    • Inadmissibility of the usual estimator for the mean of a multivariate distribution
    • Neyman, J., ed., Berkeley, CA: University of California Press
    • Stein, C. (1956). Inadmissibility of the usual estimator for the mean of a multivariate distribution. In: Neyman, J., ed. Proc. 3rd Berkeley Symp. Mathemat. Statist. Probab., Berkeley, CA: University of California Press, pp. 197-206.
    • (1956) Proc. 3rd Berkeley Symp. Mathemat. Statist. Probab. , pp. 197-206
    • Stein, C.1
  • 26
    • 33746035971 scopus 로고    scopus 로고
    • The max-min hill-climbing Bayesian network structure learning algorithm
    • Tsamardinos, I., Brown, L. E., Aliferis, C. F. (2006). The max-min hill-climbing Bayesian network structure learning algorithm. Machi. Learn. 65(1):31-78.
    • (2006) Machi. Learn. , vol.65 , Issue.1 , pp. 31-78
    • Tsamardinos, I.1    Brown, L.E.2    Aliferis, C.F.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.