On Inclusion-Driven Learning of Bayesian Networks
暂无分享,去创建一个
[1] P. Green,et al. Decomposable graphical Gaussian model determination , 1999 .
[2] L. Wasserman,et al. Computing Bayes Factors by Combining Simulation and Asymptotic Approximations , 1997 .
[3] Robert Castelo,et al. Improved learning of Bayesian networks , 2001, UAI.
[4] M. Frydenberg. The chain graph Markov property , 1990 .
[5] Moninder Singh,et al. An Algorithm for the Construction of Bayesian Network Structures from Data , 1993, UAI.
[6] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .
[7] T. Havránek. A Procedure for Model Search in Multidimensional Contingency Tables , 1984 .
[8] Milan Studený,et al. On characterizing Inclusion of Bayesian Networks , 2001, UAI.
[9] David Madigan,et al. On the relation between conditional independence models determined by finite distributive lattices and by directed acyclic graphs , 1995 .
[10] D. Madigan,et al. Bayesian model averaging and model selection for markov equivalence classes of acyclic digraphs , 1996 .
[11] Judea Pearl,et al. Equivalence and Synthesis of Causal Models , 1990, UAI.
[12] Christopher Meek,et al. Learning Bayesian Networks with Discrete Variables from Data , 1995, KDD.
[13] Remco R. Bouckaert,et al. Optimizing Causal Orderings for Generating DAGs from Data , 1992, UAI.
[14] Gregory F. Cooper,et al. The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks , 1989, AIME.
[15] Judea Pearl,et al. The Logic of Representing Dependencies by Directed Graphs , 1987, AAAI.
[16] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[17] Tom Burr,et al. Causation, Prediction, and Search , 2003, Technometrics.
[18] Nir Friedman,et al. Being Bayesian about Network Structure , 2000, UAI.
[19] Eric R. Ziegel,et al. Multivariate Statistical Modelling Based on Generalized Linear Models , 2002, Technometrics.
[20] David Heckerman,et al. Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.
[21] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[22] Steffen L. Lauritzen,et al. Independence properties of directed markov fields , 1990, Networks.
[23] C. Meek,et al. Graphical models: selecting causal and statistical models , 1997 .
[24] Michael D. Perlman,et al. Enumerating Markov Equivalence Classes of Acyclic Digraph Models , 2001, UAI.
[25] A. J. Feelders,et al. MAMBO: Discovering Association Rules Based on Conditional Independencies , 2001, IDA.
[26] David Maxwell Chickering,et al. Optimal Structure Identification With Greedy Search , 2003, J. Mach. Learn. Res..
[27] Edward H. Herskovits,et al. Computer-based probabilistic-network construction , 1992 .
[28] Fabio Gagliardi Cozman,et al. Random Generation of Bayesian Networks , 2002, SBIA.
[29] S. Chib,et al. Understanding the Metropolis-Hastings Algorithm , 1995 .
[30] David Maxwell Chickering,et al. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.
[31] Adrian F. M. Smith,et al. Bayesian computation via the gibbs sampler and related markov chain monte carlo methods (with discus , 1993 .
[32] Gregory F. Cooper,et al. A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.
[33] Michel Mouchart,et al. Discussion on "Conditional independence in statistitical theory" by A.P. Dawid , 1979 .
[34] Paolo Giudici,et al. Association Models for Web Mining , 2004, Data Mining and Knowledge Discovery.
[35] Paolo Giudici,et al. Improving Markov Chain Monte Carlo Model Search for Data Mining , 2004, Machine Learning.
[36] Judea Pearl,et al. Probabilistic reasoning in intelligent systems , 1988 .
[37] D. Madigan,et al. A characterization of Markov equivalence classes for acyclic digraphs , 1997 .
[38] Michael I. Jordan. Graphical Models , 1998 .
[39] J. York,et al. Bayesian Graphical Models for Discrete Data , 1995 .
[40] D. Edwards,et al. A fast procedure for model search in multidimensional contingency tables , 1985 .
[41] David Heckerman,et al. Learning Bayesian Networks: Search Methods and Experimental Results , 1995 .
[42] Wray L. Buntine. Theory Refinement on Bayesian Networks , 1991, UAI.
[43] David Maxwell Chickering,et al. Learning Equivalence Classes of Bayesian Network Structures , 1996, UAI.
[44] D. Madigan,et al. On the Markov Equivalence of Chain Graphs, Undirected Graphs, and Acyclic Digraphs , 1997 .
[45] David Draper,et al. Assessment and Propagation of Model Uncertainty , 2011 .
[46] Robert Castelo,et al. Learning Essential Graph Markov Models From Data , 2002, Probabilistic Graphical Models.
[47] Kai Lai Chung,et al. Markov Chains with Stationary Transition Probabilities , 1961 .
[48] David Maxwell Chickering,et al. A Transformational Characterization of Equivalent Bayesian Network Structures , 1995, UAI.
[49] A. P. Dawid,et al. Independence properties of directed Markov fields. Networks, 20, 491-505 , 1990 .
[50] G. Melançon,et al. Random generation of dags for graph drawing , 2000 .
[51] Pedro Larrañaga,et al. Learning Bayesian network structures by searching for the best ordering with genetic algorithms , 1996, IEEE Trans. Syst. Man Cybern. Part A.