Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
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[1] C. N. Liu,et al. Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.
[2] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[3] Edward H. Shortliffe,et al. A model of inexact reasoning in medicine , 1990 .
[4] Glenn Shafer,et al. A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.
[5] R. W. Robinson. Counting unlabeled acyclic digraphs , 1977 .
[6] A. Tversky,et al. The framing of decisions and the psychology of choice. , 1981, Science.
[7] Scott M. Olmsted. On representing and solving decision problems , 1983 .
[8] N. Wermuth,et al. Graphical and recursive models for contingency tables , 1983 .
[9] Judea Pearl,et al. A Computational Model for Causal and Diagnostic Reasoning in Inference Systems , 1983, IJCAI.
[10] David Heckerman,et al. Probabilistic Interpretation for MYCIN's Certainty Factors , 1990, UAI.
[11] C. Robert Kenley. INFLUENCE DIAGRAM MODELS WITH CONTINUOUS VARIABLES , 1986 .
[12] A. F. Smith,et al. Statistical analysis of finite mixture distributions , 1986 .
[13] Ross D. Shachter. Evaluating Influence Diagrams , 1986, Oper. Res..
[14] David J. Spiegelhalter,et al. Local computations with probabilities on graphical structures and their application to expert systems , 1990 .
[15] Kristian G. Olesen,et al. HUGIN - A Shell for Building Bayesian Belief Universes for Expert Systems , 1989, IJCAI.
[16] Steen Andreassen,et al. A munin network for the median nerve - a case study on loops , 1989, Appl. Artif. Intell..
[17] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[18] Dan Geiger,et al. Identifying independence in bayesian networks , 1990, Networks.
[19] Steffen L. Lauritzen,et al. Bayesian updating in causal probabilistic networks by local computations , 1990 .
[20] N. Wermuth,et al. On Substantive Research Hypotheses, Conditional Independence Graphs and Graphical Chain Models , 1990 .
[21] David J. Spiegelhalter,et al. Sequential updating of conditional probabilities on directed graphical structures , 1990, Networks.
[22] M. Frydenberg. The chain graph Markov property , 1990 .
[23] Gregory F. Cooper,et al. The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks , 1990, Artif. Intell..
[24] Steffen L. Lauritzen,et al. Independence properties of directed markov fields , 1990, Networks.
[25] P. Spirtes,et al. An Algorithm for Fast Recovery of Sparse Causal Graphs , 1991 .
[26] Kathryn B. Laskey. Conflict and Surprise: Heuristics for Model Revision , 1994, UAI.
[27] L. Zadeh,et al. Fuzzy Logic for the Management of Uncertainty , 1992 .
[28] Gregory F. Cooper,et al. A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .
[29] Pat Langley,et al. An Analysis of Bayesian Classifiers , 1992, AAAI.
[30] Ross D. Shachter,et al. Decision Making Using Probabilistic Inference Methods , 1992, UAI.
[31] S. Lauritzen. Propagation of Probabilities, Means, and Variances in Mixed Graphical Association Models , 1992 .
[32] Judea Pearl,et al. An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation , 1992, UAI.
[33] A. P. Dawid,et al. Applications of a general propagation algorithm for probabilistic expert systems , 1992 .
[34] Henri Jacques Suermondt,et al. Explanation in Bayesian belief networks , 1992 .
[35] David Heckerman,et al. Causal Independence for Knowledge Acquisition and Inference , 1993, UAI.
[36] Sampath Srinivas,et al. A Generalization of the Noisy-Or Model , 1993, UAI.
[37] Francisco Javier Díez,et al. Parameter adjustment in Bayes networks. The generalized noisy OR-gate , 1993, UAI.
[38] Michael Luby,et al. Approximating Probabilistic Inference in Bayesian Belief Networks is NP-Hard , 1993, Artif. Intell..
[39] Kathryn B. Laskey. Sensitivity analysis for probability assessments in Bayesian networks , 1995, IEEE Trans. Syst. Man Cybern..
[40] Frank Jensen,et al. From Influence Diagrams to junction Trees , 1994, UAI.
[41] David Heckerman,et al. A New Look at Causal Independence , 1994, UAI.
[42] Frank Jensen,et al. Optimal junction Trees , 1994, UAI.
[43] Gregory M. Provan,et al. Knowledge Engineering for Large Belief Networks , 1994, UAI.
[44] Young-Gyun Kim,et al. On the Detection of Conflicts in Diagnostic Bayesian Networks Using Abstraction , 1995, UAI.
[45] S. Lauritzen. The EM algorithm for graphical association models with missing data , 1995 .
[46] David Maxwell Chickering,et al. Learning Bayesian Networks is NP-Complete , 2016, AISTATS.
[47] Christopher Meek,et al. Causal inference and causal explanation with background knowledge , 1995, UAI.
[48] Uffe Kjærulff,et al. dHugin: a computational system for dynamic time-sliced Bayesian networks , 1995 .
[49] Avi Pfeffer,et al. Object-Oriented Bayesian Networks , 1997, UAI.
[50] Enrique F. Castillo,et al. Sensitivity analysis in discrete Bayesian networks , 1997, IEEE Trans. Syst. Man Cybern. Part A.
[51] Charles M. Grinstead,et al. Introduction to probability , 1999, Statistics for the Behavioural Sciences.
[52] Yan Lin,et al. Computational Advantages of Relevance Reasoning in Bayesian Belief Networks , 1997, UAI.
[53] Kathryn B. Laskey,et al. Network Fragments: Representing Knowledge for Constructing Probabilistic Models , 1997, UAI.
[54] David Heckerman,et al. A Tutorial on Learning with Bayesian Networks , 1998, Learning in Graphical Models.
[55] Nir Friedman,et al. The Bayesian Structural EM Algorithm , 1998, UAI.
[56] Anders L. Madsen,et al. ProbSy--A System for the Calculation of Probabilities in the Card Game Bridge , 1998, FLAIRS.
[57] Ross D. Shachter. Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams) , 1998, UAI.
[58] L. C. van der Gaag,et al. Practicable sensitivity analysis of Bayesian belief networks , 1998 .
[59] Xavier Boyen,et al. Tractable Inference for Complex Stochastic Processes , 1998, UAI.
[60] Anders L. Madsen,et al. LAZY Propagation: A Junction Tree Inference Algorithm Based on Lazy Evaluation , 1999, Artif. Intell..
[61] Silja Renooij,et al. Talking probabilities: communicating probabilistic information with words and numbers , 1999, Int. J. Approx. Reason..
[62] Thomas D. Nielsen,et al. Welldefined Decision Scenarios , 1999, UAI.
[63] Richard Scheines,et al. Causation, Prediction, and Search, Second Edition , 2000, Adaptive computation and machine learning.
[64] J. Pearl. Causality: Models, Reasoning and Inference , 2000 .
[65] Martin Neil,et al. Building large-scale Bayesian networks , 2000, The Knowledge Engineering Review.
[66] Linda C. van der Gaag,et al. Making Sensitivity Analysis Computationally Efficient , 2000, UAI.
[67] Steffen L. Lauritzen,et al. Stable local computation with conditional Gaussian distributions , 2001, Stat. Comput..
[68] Anders L. Madsen,et al. Solving Influence Diagrams using HUGIN, Shafer-Shenoy and Lazy Propagation , 2001, UAI.
[69] Finn V. Jensen,et al. Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.
[70] Steffen L. Lauritzen,et al. Representing and Solving Decision Problems with Limited Information , 2001, Manag. Sci..
[71] Silja Renooij,et al. Probability elicitation for belief networks: issues to consider , 2001, The Knowledge Engineering Review.
[72] Silja Renooij,et al. Analysing Sensitivity Data from Probabilistic Networks , 2001, UAI.
[73] Robert G. Cowell,et al. Conditions Under Which Conditional Independence and Scoring Methods Lead to Identical Selection of Bayesian Network Models , 2001, UAI.
[74] Adnan Darwiche,et al. When do Numbers Really Matter? , 2001, UAI.
[75] Thomas D. Nielsen. Decomposition of Influence Diagrams , 2001, ECSQARU.
[76] David Maxwell Chickering,et al. Optimal Structure Identification With Greedy Search , 2003, J. Mach. Learn. Res..
[77] Adnan Darwiche,et al. A distance measure for bounding probabilistic belief change , 2002, Int. J. Approx. Reason..
[78] Nevin Lianwen Zhang,et al. Hierarchical latent class models for cluster analysis , 2002, J. Mach. Learn. Res..
[79] Finn Verner Jensen,et al. An Extension Of Lazy Evaluation For Influence Diagrams Avoiding Redundant Variables In The Potentials , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[80] Haiqin Wang,et al. Using Sensitivity Analysis for Selective Parameter Update in Bayesian Network Learning , 2002 .
[81] Silja Renooij,et al. Probabilities for a probabilistic network: a case study in oesophageal cancer , 2002, Artif. Intell. Medicine.
[82] Christopher Meek,et al. Monotone DAG Faithfulness: A Bad Assumption , 2003 .
[83] Prakash P. Shenoy,et al. Decision making with hybrid influence diagrams using mixtures of truncated exponentials , 2004, Eur. J. Oper. Res..
[84] Nir Friedman,et al. Bayesian Network Classifiers , 1997, Machine Learning.
[85] Anders L. Madsen,et al. A Differential Semantics of Lazy AR Propagation , 2005, UAI.
[86] Anders L. Madsen,et al. Solving linear-quadratic conditional Gaussian influence diagrams , 2005, Int. J. Approx. Reason..
[87] Anders L. Madsen,et al. The Hugin Tool for Probabilistic Graphical Models , 2005, Int. J. Artif. Intell. Tools.
[88] Prakash P. Shenoy. Inference in Hybrid Bayesian Networks Using Mixtures of Gaussians , 2006, UAI.
[89] W. R. Shao,et al. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis , 2008 .
[90] Arthur P. Dempster,et al. A Generalization of Bayesian Inference , 1968, Classic Works of the Dempster-Shafer Theory of Belief Functions.
[91] Adnan Darwiche,et al. Modeling and Reasoning with Bayesian Networks , 2009 .
[92] Norman Fenton,et al. Modelling mutually exclusive causes in Bayesian networks , 2011 .