Learning tractable Bayesian networks in the space of elimination orders
暂无分享,去创建一个
[1] Jacques Carlier,et al. Heuristic and metaheuristic methods for computing graph treewidth , 2004, RAIRO Oper. Res..
[2] Concha Bielza,et al. Learning Bayesian networks with low inference complexity , 2016, Progress in Artificial Intelligence.
[3] Michael I. Jordan,et al. Thin Junction Trees , 2001, NIPS.
[4] Dan Geiger,et al. A Practical Algorithm for Finding Optimal Triangulations , 1997, AAAI/IAAI.
[5] J. Shaffer. Modified Sequentially Rejective Multiple Test Procedures , 1986 .
[6] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[7] Stephen Gould,et al. Learning Bounded Treewidth Bayesian Networks , 2008, NIPS.
[8] Duc Truong Pham,et al. Unsupervised training of Bayesian networks for data clustering , 2009, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[9] Michael Luby,et al. Approximating Probabilistic Inference in Bayesian Belief Networks is NP-Hard , 1993, Artif. Intell..
[10] Nir Friedman,et al. Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning , 2009 .
[11] Jens Lagergren,et al. Learning Bounded Tree-width Bayesian Networks using Integer Linear Programming , 2014, AISTATS.
[12] Concha Bielza,et al. Discrete Bayesian Network Classifiers , 2014, ACM Comput. Surv..
[13] Robert E. Tarjan,et al. Algorithmic Aspects of Vertex Elimination on Graphs , 1976, SIAM J. Comput..
[14] Guy Van den Broeck,et al. Tractable Learning for Complex Probability Queries , 2015, NIPS.
[15] S. Holm. A Simple Sequentially Rejective Multiple Test Procedure , 1979 .
[16] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[17] Kevin Grant,et al. Methods for constructing balanced elimination trees and other recursive decompositions , 2006, Int. J. Approx. Reason..
[18] Pedro Larrañaga,et al. Learning Bayesian networks for clustering by means of constructive induction , 1999, Pattern Recognit. Lett..
[19] Adnan Darwiche,et al. Modeling and Reasoning with Bayesian Networks , 2009 .
[20] Guy Van den Broeck,et al. Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference , 2014, AAAI.
[21] James Cussens,et al. Bayesian network learning with cutting planes , 2011, UAI.
[22] S. García,et al. An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons , 2008 .
[23] Marco Zaffalon,et al. Learning Bayesian Networks with Thousands of Variables , 2015, NIPS.
[24] Johan Kwisthout,et al. Most probable explanations in Bayesian networks: Complexity and tractability , 2011, Int. J. Approx. Reason..
[25] Adnan Darwiche,et al. Recursive conditioning , 2001, Artif. Intell..
[26] Dimitrios M. Thilikos,et al. On exact algorithms for treewidth , 2006, TALG.
[27] Remco R. Bouckaert,et al. Probalistic Network Construction Using the Minimum Description Length Principle , 1993, ECSQARU.
[28] Ravindra K. Ahuja,et al. Network Flows: Theory, Algorithms, and Applications , 1993 .
[29] Barry W. Peyton,et al. Maximum Cardinality Search for Computing Minimal Triangulations of Graphs , 2004, Algorithmica.
[30] Gregory F. Cooper,et al. A Bayesian Method for Constructing Bayesian Belief Networks from Databases , 1991, UAI.
[31] David R. Karger,et al. Learning Markov networks: maximum bounded tree-width graphs , 2001, SODA '01.
[32] Concha Bielza,et al. International Journal of Approximate Reasoning Tractability of most probable explanations in multidimensional Bayesian network classifiers ✩ , 2022 .
[33] Pedro Larrañaga,et al. Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms , 1997, Stat. Comput..
[34] Qiang Ji,et al. Advances in Learning Bayesian Networks of Bounded Treewidth , 2014, NIPS.
[35] Pedro M. Domingos,et al. Learning Arithmetic Circuits , 2008, UAI.
[36] David Maxwell Chickering,et al. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.
[37] Janne H. Korhonen,et al. Exact Learning of Bounded Tree-width Bayesian Networks , 2013, AISTATS.
[38] Carlos Guestrin,et al. Efficient Principled Learning of Thin Junction Trees , 2007, NIPS.
[39] Fedor V. Fomin,et al. Exact (Exponential) Algorithms for Treewidth and Minimum Fill-In , 2004, ICALP.
[40] Marco Zaffalon,et al. Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables , 2016, NIPS.
[41] Judea Pearl,et al. A Constraint-Propagation Approach to Probabilistic Reasoning , 1985, UAI.
[42] Hans L. Bodlaender. A linear time algorithm for finding tree-decompositions of small treewidth , 1993, STOC '93.
[43] Concha Bielza,et al. Multi-dimensional classification with Bayesian networks , 2011, Int. J. Approx. Reason..
[44] Wai Lam,et al. LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE , 1994, Comput. Intell..
[45] Ross D. Shachter. Evidence Absorption and Propagation through Evidence Reversals , 2013, UAI.
[46] Arie M. C. A. Koster,et al. Frequency assignment : models and algorithms , 1999 .
[47] Marco Zaffalon,et al. Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets , 2018, Int. J. Approx. Reason..
[48] Dafna Shahaf,et al. Learning Thin Junction Trees via Graph Cuts , 2009, AISTATS.
[49] Gregory F. Cooper,et al. The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks , 1990, Artif. Intell..
[50] Fedor V. Fomin,et al. Treewidth computation and extremal combinatorics , 2012 .
[51] Jesse Davis,et al. Learning Markov Network Structure with Decision Trees , 2010, 2010 IEEE International Conference on Data Mining.
[52] Gregory F. Cooper,et al. A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.
[53] Derek G. Corneil,et al. Complexity of finding embeddings in a k -tree , 1987 .
[54] Qiang Ji,et al. Efficient learning of Bayesian networks with bounded tree-width , 2017, Int. J. Approx. Reason..
[55] Judea Pearl,et al. A Computational Model for Causal and Diagnostic Reasoning in Inference Systems , 1983, IJCAI.
[56] Craig Boutilier,et al. Context-Specific Independence in Bayesian Networks , 1996, UAI.
[57] Judea Pearl,et al. Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach , 1982, AAAI.
[58] Arie M. C. A. Koster,et al. Treewidth computations I. Upper bounds , 2010, Inf. Comput..
[59] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[60] Uffe Kjærulff. Optimal decomposition of probabilistic networks by simulated annealing , 1992 .
[61] Nir Friedman,et al. The Bayesian Structural EM Algorithm , 1998, UAI.
[62] H. Markowitz. The Elimination form of the Inverse and its Application to Linear Programming , 1957 .
[63] Jesse Davis,et al. Markov Network Structure Learning: A Randomized Feature Generation Approach , 2012, AAAI.
[64] H. Akaike. A new look at the statistical model identification , 1974 .
[65] Prakash P. Shenoy,et al. Axioms for probability and belief-function proagation , 1990, UAI.
[66] Adnan Darwiche,et al. A differential approach to inference in Bayesian networks , 2000, JACM.
[67] Brandon M. Malone,et al. Learning Optimal Bounded Treewidth Bayesian Networks via Maximum Satisfiability , 2014, AISTATS.
[68] Robert E. Tarjan,et al. Simple Linear-Time Algorithms to Test Chordality of Graphs, Test Acyclicity of Hypergraphs, and Selectively Reduce Acyclic Hypergraphs , 1984, SIAM J. Comput..
[69] Nevin L. Zhang,et al. A simple approach to Bayesian network computations , 1994 .
[70] Linda C. van der Gaag,et al. Multi-dimensional Bayesian Network Classifiers , 2006, Probabilistic Graphical Models.