Reading Dependencies from Polytree-Like Bayesian Networks

作者: Jose M. Pena

DOI:

关键词:

摘要: We present a graphical criterion for reading dependencies from the minimal directed independence map G of graphoid p when is polytree and satisfies composition weak transitivity. prove that sound complete. argue assuming transitivity not too restrictive.

参考文章(18)
Dan Geiger, Ann Becker, Christopher Meek, Perfect tree-like Markovian distributions uncertainty in artificial intelligence. pp. 19- 23 ,(2000)
Juliane Schäfer, Korbinian Strimmer, Learning Large-Scale Graphical Gaussian Models from Genomic Data AIP Conference Proceedings. ,vol. 776, pp. 263- 276 ,(2005) , 10.1063/1.1985393
Nir Friedman, Stuart Russell, Kevin Murphy, Learning the structure of dynamic probabilistic networks uncertainty in artificial intelligence. pp. 139- 147 ,(1998)
Christopher Meek, Strong completeness and faithfulness in Bayesian networks uncertainty in artificial intelligence. pp. 411- 418 ,(1995)
ALLISTER BERNARD, ALEXANDER J. HARTEMINK, Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data. pacific symposium on biocomputing. pp. 459- 470 ,(2004) , 10.1142/9789812702456_0044
Juliane Schäfer, Korbinian Strimmer, A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics Statistical Applications in Genetics and Molecular Biology. ,vol. 4, pp. 1- 32 ,(2005) , 10.2202/1544-6115.1175
Alberto Roverato, Robert Castelo, A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n Journal of Machine Learning Research. ,vol. 7, pp. 2621- 2650 ,(2006)