On the Approximation of a Discrete Multivariate Probability Distribution Using the New Concept of t -Cherry Junction Tree
暂无分享,去创建一个
[1] Steffen L. Lauritzen,et al. Bayesian updating in causal probabilistic networks by local computations , 1990 .
[2] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[3] Tamás Szántai,et al. Probability bounds given by hypercherry trees , 2002, Optim. Methods Softw..
[4] E. B. Andersen,et al. Information Science and Statistics , 1986 .
[5] C. N. Liu,et al. Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.
[6] David J. Spiegelhalter,et al. Probabilistic Networks and Expert Systems , 1999, Information Science and Statistics.
[7] I. Csiszár. $I$-Divergence Geometry of Probability Distributions and Minimization Problems , 1975 .
[8] Solomon Kullback,et al. Information Theory and Statistics , 1960 .
[9] Adnan Darwiche,et al. Inference in belief networks: A procedural guide , 1996, Int. J. Approx. Reason..
[10] Enrique F. Castillo,et al. Expert Systems and Probabilistic Network Models , 1996, Monographs in Computer Science.
[11] András Prékopa,et al. Probability Bounds with Cherry Trees , 2001, Math. Oper. Res..
[12] Frank Hutter,et al. Incremental Thin Junction Trees for Dynamic Bayesian networks , 2004 .
[13] Finn V. Jensen,et al. Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.
[14] Jie Cheng,et al. An Algorithm for Bayesian Belief Network Construction from Data , 2004 .
[15] Luis M. de Campos,et al. An Algorithm for Finding Minimum d-Separating Sets in Belief Networks , 1996, UAI.