A Roadmap to Research on Bayesian Networks and other Decomposable Probabilistic Models

This paper is a listing of literature on Bayesian Networks and related graphical probability models. It is my own personal notes and is continually changing, but feel free to grab a copy. If you have additions or corrections, please let me know. RG: This has NOT been updated since around Dec'96.] Innuence Diagrams include decision nodes and a value (utility) node.

[1]  Serafín Moral,et al.  An axiomatic framework for propagating uncertainty in directed acyclic networks , 1993, Int. J. Approx. Reason..

[2]  Solomon Eyal Shimony,et al.  Finding MAPs for Belief Networks is NP-Hard , 1994, Artif. Intell..

[3]  A. Dawid.  Conditional Independence for Statistical Operations , 1980 .

[4]  Ross D. Shachter,et al.  Simulation Approaches to General Probabilistic Inference on Belief Networks , 2013, UAI.

[5]  Judea Pearl,et al.  Causal inference from indirect experiments , 1995, Artif. Intell. Medicine.

[6]  N. Wermuth,et al.  On Substantive Research Hypotheses, Conditional Independence Graphs and Graphical Chain Models , 1990 .

[7]  Hon-Son Don,et al.  A robust continuous model for evidential reasoning , 1994, J. Intell. Robotic Syst..

[8]  Judea Pearl,et al.  Embracing Causality in Formal Reasoning , 1987, AAAI.

[9]  Enrique F. Castillo,et al.  Parametric Structure of Probabilities in Bayesian Networks , 1995, ECSQARU.

[10]  Eric Horvitz,et al.  The myth of modularity in rule-based systems for reasoning with uncertainty , 1986, UAI.

[11]  Serafín Moral,et al.  Propagation of Uncertainty in Dependence Graphs , 1991, ECSQARU.

[12]  Judea Pearl,et al.  A Constraint-Propagation Approach to Probabilistic Reasoning , 1985, UAI.

[13]  Wray L. BuntineRIACS.  Theory Reenement on Bayesian Networks , 1991 .

[14]  Judea Pearl,et al.  Deciding Morality of Graphs is NP-complete , 1993, UAI.

[15]  John S. Breese,et al.  CONSTRUCTION OF BELIEF AND DECISION NETWORKS , 1992, Comput. Intell..

[16]  David J. Spiegelhalter,et al.  Sequential updating of conditional probabilities on directed graphical structures , 1990, Networks.

[17]  Ulrich G. Oppel,et al.  Examples of Causal Probabilistic Expert Systems , 1993, ECSQARU.

[18]  David Maxwell Chickering,et al.  A Transformational Characterization of Equivalent Bayesian Network Structures , 1995, UAI.

[19]  Nevin Lianwen Zhang,et al.  Sidestepping the Triangulation Problem in Bayesian Net Computations , 1992, UAI 1992.

[20]  John Mark Agosta.  "Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-OR" Models , 1994, UAI.

[21]  Jaswinder Pal Singh,et al.  Sensitivities: An Alternative to Conditional Probabilities for Bayesian Belief Networks , 1995, UAI.

[22]  Prakash P. Shenoy,et al.  Axioms for probability and belief-function proagation , 1990, UAI.

[23]  P. Spirtes,et al.  An Algorithm for Fast Recovery of Sparse Causal Graphs , 1991 .

[24]  Khaled Mellouli,et al.  Propagating belief functions in qualitative Markov trees , 1987, Int. J. Approx. Reason..

[25]  David J. Spiegelhalter,et al.  Probabilistic Reasoning in Predictive Expert Systems , 1985, UAI.

[26]  Bruce D'Ambrosio,et al.  Symbolic Probabilistic Inference in Large BN20 Networks , 1994, UAI.

[27]  David J. Spiegelhalter,et al.  Bayesian networks for patient monitoring , 1992, Artif. Intell. Medicine.

[28]  Ross D. Shachter,et al.  Global Conditioning for Probabilistic Inference in Belief Networks , 1994, UAI.

[29]  Joe Suzuki,et al.  A Construction of Bayesian Networks from Databases Based on an MDL Principle , 1993, UAI.

[30]  Henrik I. Christensen,et al.  Qualitative recognition using Bayesian reasoning , 1994 .

[31]  Robert P. Goldman,et al.  From knowledge bases to decision models , 1992, The Knowledge Engineering Review.

[32]  Ross D. Shachter,et al.  Decision Making Using Probabilistic Inference Methods , 1992, UAI.

[33]  Fahiem Bacchus.  Using First-Order Probability Logic for the Construction of Bayesian Networks , 1993, UAI.

[34]  Nevin Lianwen Zhang,et al.  Intercausal Independence and Heterogeneous Factorization , 1994, UAI.

[35]  Dan Geiger,et al.  Approximation Algorithms for the Loop Cutset Problem , 1994, UAI.

[36]  Ross D. Shachter,et al.  Fusion and Propagation with Multiple Observations in Belief Networks , 1991, Artif. Intell..

[37]  Judea Pearl,et al.  Belief Networks Revisited , 1993, Artif. Intell..

[38]  Eric Horvitz,et al.  Reasoning about the Value of Decision-Model Refinement: Methods and Application , 1993, UAI.

[39]  David Heckerman,et al.  Probabilistic similarity networks , 1991, Networks.

[40]  Simon Parsons,et al.  On reasoning in networks with qualitative uncertainty , 1993, UAI.

[41]  Michael I. Jordan,et al.  Probabilistic Independence Networks for Hidden Markov Probability Models , 1997, Neural Computation.

[42]  Stuart J. Russell,et al.  Stochastic simulation algorithms for dynamic probabilistic networks , 1995, UAI.

[43]  Khaled Mellouli,et al.  On the propagation of beliefs in networks using the Dempster-Shafer theory of evidence , 1987 .

[44]  L. Phillips,et al.  A socio-technical approach to assessing human reliability , 1990 .

[45]  Peter Spirtes,et al.  Directed Cyclic Graphical Representations of Feedback Models , 1995, UAI.

[46]  R. Martin Chavez,et al.  Approximating Probabilistic Inference in Bayesian Belief Networks , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[47]  C. Robert Kenley.  INFLUENCE DIAGRAM MODELS WITH CONTINUOUS VARIABLES , 1986 .

[48]  Judea Pearl,et al.  GRAPHOIDS: A Graph-based logic for reasoning about relevance relations , 1985 .

[49]  Christopher Meek.  Relating graphical frameworks : undirected, directed acyclic and chain graph models , 1995 .

[50]  Richard E. Neapolitan,et al.  An Implementation of a Method for Computing the Uncertainty in Inferred Probabilities in Belief Networks , 1993, UAI.

[51]  Kristian G. Olesen,et al.  Causal Probabilistic Networks with Both Discrete and Continuous Variables , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[52]  A. Dawid.  Conditional Independence in Statistical Theory , 1979 .

[53]  J. Pearl.  Causal diagrams for empirical research , 1995 .

[54]  Gregory F. Cooper,et al.  NESTOR: A Computer-Based Medical Diagnostic Aid That Integrates Causal and Probabilistic Knowledge. , 1984 .

[55]  Adnan Darwiche.  Conditioning Methods for Exact and Approximate Inference in Causal Networks , 1995, UAI 1995.

[56]  V. Isham.  An Introduction to Spatial Point Processes and Markov Random Fields , 1981 .

[57]  Mark A. Kramer,et al.  GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain Systems Modeled with Bayesian Belief Networks , 1993, UAI.

[58]  Gregory M. Provan,et al.  Dynamic Network Construction and Updating Techniques for the Diagnosis of Acute Abdominal Pain , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[59]  Radim Jirousek,et al.  A survey of methods used in probabilistic expert systems for knowledge integration , 1990, Knowl. Based Syst..

[60]  Scott A. Musman,et al.  A Study of Scaling Issues in Bayesian Belief Networks for Ship Classification , 1993, UAI.

[61]  Peter Haddawy,et al.  A Theoretical Framework for Context-Sensitive Temporal Probability Model Construction with Application to Plan Projection , 1995, UAI.

[62]  Thomas L. Dean,et al.  A Model for Projection and Action , 1989, IJCAI.

[63]  Uffe Kjærulff,et al.  dHugin: a computational system for dynamic time-sliced Bayesian networks , 1995 .

[64]  Prakash P. Shenoy,et al.  Propagating belief functions in AND‐trees , 1995, Int. J. Intell. Syst..

[65]  Max Henrion,et al.  Search-Based Methods to Bound Diagnostic Probabilities in Very Large Belief Nets , 1991, UAI.

[66]  Bjørnar Tessem,et al.  Interval probability propagation , 1992, Int. J. Approx. Reason..

[67]  Michael I. Jordan,et al.  Mean Field Theory for Sigmoid Belief Networks , 1996, J. Artif. Intell. Res..

[68]  Michael P. Wellman.  Fundamental Concepts of Qualitative Probabilistic Networks , 1990, Artif. Intell..

[69]  Judea Pearl,et al.  Structural and probabilistic causality. , 1996 .

[70]  Nevin Lianwen Zhang,et al.  A computational theory of decision networks , 1993, Int. J. Approx. Reason..

[71]  Gregory F. Cooper,et al.  A randomized approximation algorithm for probabilistic inference on bayesian belief networks , 1990, Networks.

[72]  Gregory Provan,et al.  Tradeoffs in Knowledge-Based Construction of Probabilistic Models , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[73]  John S. Breese,et al.  Decision making with interval influence diagrams , 1990, UAI.

[74]  Judea Pearl,et al.  A Theory of Inferred Causation , 1991, KR.

[75]  David Heckerman,et al.  Causal Independence for Knowledge Acquisition and Inference , 1993, UAI.

[76]  Steffen L. Lauritzen,et al.  Bayesian updating in causal probabilistic networks by local computations , 1990 .

[77]  Eric Horvitz,et al.  Decision Analysis and Expert Systems , 1991, AI Mag..

[78]  Wray L. Buntine.  Operations for Learning with Graphical Models , 1994, J. Artif. Intell. Res..

[79]  Wai Lam,et al.  Abstraction in Bayesian Belief Networks and Automatic Discovery from Past Inference Sessions , 1994, AAAI.

[80]  Wray Buntine,et al.  A Guide to the Literature on Learning Graphical Models , 1994 .

[81]  Judea Pearl,et al.  Probabilistic Evaluation of Counterfactual Queries , 1994, AAAI.

[82]  Jitendra Malik,et al.  Automatic Symbolic Traffic Scene Analysis Using Belief Networks , 1994, AAAI.

[83]  Kathryn B. Laskey,et al.  Metareasoning and the Problem of Small Worlds , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[84]  Ross D. Shachter.  Evidence Absorption and Propagation through Evidence Reversals , 2013, UAI.

[85]  Radim Jirousek.  DECISION-TREE MODELLING OF PROBABILITY DISTRIBUTIONS1 , 1993 .

[86]  Peter Spirtes,et al.  Conditional Independence in Directed Cyclic Graphical Models for Feedback , 1994 .

[87]  Eric Horvitz,et al.  Dynamic construction and refinement of utility-based categorization models , 1994 .

[88]  Michael I. Jordan,et al.  Exploiting Tractable Substructures in Intractable Networks , 1995, NIPS.

[89]  Alessandro Saffiotti,et al.  Pulcinella: A General Tool for Propagating Uncertainty in Valuation Networks , 1991, UAI.

[90]  Max Henrion,et al.  Propagating uncertainty in bayesian networks by probabilistic logic sampling , 1986, UAI.

[91]  Kathryn B. Laskey.  Conflict and Surprise: Heuristics for Model Revision , 1994, UAI.

[92]  Stephen R. Watson.  [Probabilistic Expert Systems in Medicine: Practical Issues in Handling Uncertainty]: Comment , 1987 .

[93]  Wai Lam,et al.  Using Causal Information and Local Measures to Learn Bayesian Networks , 1993, UAI.

[94]  T. Speed,et al.  Recursive causal models , 1984, Journal of the Australian Mathematical Society. Series A. Pure Mathematics and Statistics.

[95]  Gregory F. Cooper,et al.  Bounded recursive decomposition: a search-based method for belief-network inference under limited resources , 1996, Int. J. Approx. Reason..

[96]  Uffe Kjærulff,et al.  Blocking Gibbs sampling in very large probabilistic expert systems , 1995, Int. J. Hum. Comput. Stud..

[97]  Joseph Y. Halpern,et al.  Forming Beliefs about a Changing World , 1994, AAAI.

[98]  Piero P. Bonissone,et al.  Summarizing and propagating uncertain information with triangular norms , 1990, Int. J. Approx. Reason..

[99]  Remco R. Bouckaert,et al.  Properties of Bayesian Belief Network Learning Algorithms , 1994, UAI.

[100]  R. Tibshirani,et al.  Generalized Additive Models: Some Applications , 1987 .

[101]  Ann E. Nicholson,et al.  Sensor Validation Using Dynamic Belief Networks , 1992, UAI.

[102]  Nic Wilson,et al.  A Monte-Carlo Algorithm for Dempster-Shafer Belief , 1991, UAI.

[103]  David J. Spiegelhalter,et al.  Bayesian Networks Applied to Therapy Monitoring , 1991, UAI.

[104]  Ross D. Shachter.  A Graph-Based Inference Method for Conditional Independence , 1991, UAI.

[105]  R. P. Srivastava,et al.  The belief‐function approach to aggregating audit evidence , 1995, Int. J. Intell. Syst..

[106]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[107]  Uffe Kjærulff,et al.  Reduction of Computational Complexity in Bayesian Networks Through Removal of Weak Dependences , 1994, UAI.

[108]  Peter M. Williams.  An interpretation of Shenoy and Shafer's axioms for local computation , 1990, Int. J. Approx. Reason..

[109]  Eugene Santos,et al.  A Linear Constraint Satisfaction Approach to Cost-Based Abduction , 1994, Artif. Intell..

[110]  Michael P. Wellman,et al.  Real-world applications of Bayesian networks , 1995, CACM.

[111]  Gregory M. Provan,et al.  Dynamic Network Updating Techniques for Diagnostic Reasoning , 1991, UAI.

[112]  Peter Haddawy,et al.  Answering Queries from Context-Sensitive Probabilistic Knowledge Bases , 1997, Theor. Comput. Sci..

[113]  Yang Xiang,et al.  Exploring Localization in Bayesian Networks for Large Expert Systems , 1992, UAI.

[114]  Judea Pearl,et al.  Evidential Reasoning Using Stochastic Simulation of Causal Models , 1987, Artif. Intell..

[115]  Prakash P. Shenoy,et al.  Valuation Networks and Conditional Independence , 1993, Conference on Uncertainty in Artificial Intelligence.

[116]  P Haddawy,et al.  Clinical simulation using context-sensitive temporal probability models. , 1995, Proceedings. Symposium on Computer Applications in Medical Care.

[117]  Paul Dagum,et al.  Forecasting Sleep Apnea with Dynamic Network Models , 1993, UAI.

[118]  Ross D. Shachter,et al.  Structural Controllability and Observability in Influence Diagrams , 1992, UAI.

[119]  Ronald A. Howard,et al.  Information Value Theory , 1966, IEEE Trans. Syst. Sci. Cybern..

[120]  Simon Kasif,et al.  Logarithmic-Time Updates and Queries in Probabilistic Networks , 1995, UAI.

[121]  David Heckerman,et al.  A Bayesian Approach to Learning Causal Networks , 1995, UAI.

[122]  Frank Jensen,et al.  From Influence Diagrams to junction Trees , 1994, UAI.

[123]  Bruce Abramson.  ARCO1: An Application of Belief Networks to the Oil Market , 1991, UAI.

[124]  Wolfgang Spohn,et al.  Stochastic independence, causal independence, and shieldability , 1980, J. Philos. Log..

[125]  Sumit Sarkar,et al.  Using Tree-Decomposable Structures to Approximate Belief Networks , 1993, UAI.

[126]  Hon-Son Don,et al.  A Polar Model for Evidential Reasoning , 1994, Inf. Sci..

[127]  Uue Kjjrull.  Approximation of Bayesian Networks through Edge Removals D Approximation of Bayesian Networks through Edge Removals , 1993 .

[128]  Prakash P. Shenoy,et al.  Valuation-Based Systems for Bayesian Decision Analysis , 1992, Oper. Res..

[129]  Kathryn B. Laskey.  Sensitivity analysis for probability assessments in Bayesian networks , 1995, IEEE Trans. Syst. Man Cybern..

[130]  Peter Haddawy,et al.  Probabilistic Logic Programming and Bayesian Networks , 1995, ASIAN.

[131]  Finn Verner Jensen,et al.  Cautious Propagation in Bayesian Networks , 1995, UAI.

[132]  Wray L. Buntine.  Chain graphs for learning , 1995, UAI.

[133]  S. Lauritzen.  Propagation of Probabilities, Means, and Variances in Mixed Graphical Association Models , 1992 .

[134]  Uffe Kjærulff,et al.  A Computational Scheme for Reasoning in Dynamic Probabilistic Networks , 1992, UAI.

[135]  Bruce Abramson,et al.  Some Complexity Considerations in the Combination of Belief Networks , 1993, UAI.

[136]  Hong Xu.  An Efficient Implementation of Belief Function Propagation , 1991, UAI.

[137]  Judea Pearl,et al.  Counterfactuals and Policy Analysis in Structural Models , 1995, UAI.

[138]  Paul E. Lehner,et al.  Two Procedures for Compiling Influence Diagrams , 1993, UAI.

[139]  Paul Dagum,et al.  Additive Belief-Network Models , 1993, UAI.

[140]  Kristian G. Olesen,et al.  An algebra of bayesian belief universes for knowledge-based systems , 1990, Networks.

[141]  Malene Højbjerre,et al.  Lecture Notes in Statistics 101: Linear and Graphical Models for the Multivariate Complex Normal Distribution , 1995 .

[142]  Steffen L. Lauritzen,et al.  aHUGIN: A System Creating Adaptive Causal Probabilistic Networks , 1992, UAI.

[143]  Ron Musick,et al.  Minimal Assumption Distribution Propagation in Belief Networks , 1993, UAI.

[144]  Michael P. Wellman,et al.  State-Space Abstraction for Anytime Evaluation of Probabilistic Networks , 1994, UAI.

[145]  Ross D. Shachter,et al.  Laplace's Method Approximations for Probabilistic Inference in Belief Networks with Continuous Variables , 1994, UAI.

[146]  N. Wermuth,et al.  Graphical Models for Associations between Variables, some of which are Qualitative and some Quantitative , 1989 .

[147]  P Haddawy,et al.  Preliminary investigation of a Bayesian network for mammographic diagnosis of breast cancer. , 1995, Proceedings. Symposium on Computer Applications in Medical Care.

[148]  伏信 進矢.  アイオワで computational な夏 , 2007 .

[149]  Constantin F. Aliferis,et al.  An Evaluation of an Algorithm for Inductive Learning of Bayesian Belief Networks Using Simulated Data Sets , 1994, UAI.

[150]  Judea Pearl,et al.  Bayesian decision methods , 1990 .

[151]  Gregory F. Cooper,et al.  A Bayesian Method for Constructing Bayesian Belief Networks from Databases , 1991, UAI.

[152]  A. Saffiotti,et al.  Inference-Driven Construction of Valuation Systems from First-Order Clauses , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[153]  Young-Gyun Kim,et al.  On the Detection of Conflicts in Diagnostic Bayesian Networks Using Abstraction , 1995, UAI.

[154]  Moninder Singh,et al.  An Algorithm for the Construction of Bayesian Network Structures from Data , 1993, UAI.

[155]  Ann E. Nicholson,et al.  Using Goals to Find Plans with High Expected Utility , 1993 .

[156]  R. A. Stone.  The Assumptions on Which Causal Inferences Rest , 1993 .

[157]  Frank Jensen,et al.  Approximations in Bayesian Belief Universe for Knowledge Based Systems , 2013, UAI 1990.

[158]  Finn Verner Jensen,et al.  DrHugin A system for value of information in Bayesian networks , 1994 .

[159]  Nevin Lianwen Zhang,et al.  Incremental computation of the value of perfect information in stepwise-decomposable influence diagrams , 1993, UAI.

[160]  David Heckerman,et al.  Decision-theoretic case-based reasoning , 1994, IEEE Trans. Syst. Man Cybern. Part A.

[161]  Ross D. Shachter,et al.  Symbolic Probabilistic Inference in Belief Networks , 1990, AAAI.

[162]  Michael P. Wellman.  Graphical inference in qualitative probabilistic networks , 1990, Networks.

[163]  Eric Horvitz,et al.  An Approximate Nonmyopic Computation for Value of Information , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[164]  Alexander Balke,et al.  Probabilistic counterfactuals: semantics, computation, and applications , 1996 .

[165]  Luis M. de Campos,et al.  Independence Concepts for Convex Sets of Probabilities , 1995, UAI.

[166]  David Heckerman,et al.  Troubleshooting Under Uncertainty , 1994 .

[167]  Eric Horvitz,et al.  Dynamic Network Models for Forecasting , 1992, UAI.

[168]  Judea Pearl,et al.  An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation , 1992, UAI.

[169]  A. P. Dawid,et al.  Applications of a general propagation algorithm for probabilistic expert systems , 1992 .

[170]  T. Speed,et al.  Decomposable graphs and hypergraphs , 1984, Journal of the Australian Mathematical Society. Series A. Pure Mathematics and Statistics.

[171]  Riccardo Bellazzi,et al.  Temporal Reasoning with Probabilities , 1989, UAI 1989.

[172]  Ross D. Shachter,et al.  A Decision-based View of Causality , 1994, UAI.

[173]  Bruce D'Ambrosio,et al.  Local Expression Languages for Probabilistic Dependence: a Preliminary Report , 1994, UAI 1994.

[174]  Sampath Srinivas,et al.  A Generalization of the Noisy-Or Model , 1993, UAI.

[175]  Judea Pearl,et al.  The Logic of Representing Dependencies by Directed Graphs , 1987, AAAI.

[176]  David J. Spiegelhalter,et al.  Sequential Model Criticism in Probabilistic Expert Systems , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[177]  David J. Spiegelhalter,et al.  Bayesian analysis in expert systems , 1993 .

[178]  Eugene Charniak,et al.  Bayesian Networks without Tears , 1991, AI Mag..

[179]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[180]  Frank Jensen,et al.  Optimal junction Trees , 1994, UAI.