Access the full text.
Sign up today, get DeepDyve free for 14 days.
Angelika Kimmig, Guy Broeck, L. Raedt (2012)
Algebraic model countingArXiv, abs/1211.4475
P. Walley (2000)
Towards a unified theory of imprecise probabilityInt. J. Approx. Reason., 24
Phillip Porras, P. Neumann (1997)
EMERALD: Event Monitoring Enabling Responses to Anomalous Live DisturbancesInformation Systems Security
M. Ramoni, P. Sebastiani (1998)
Parameter Estimation in Bayesian Networks from Incomplete DatabasesIntell. Data Anal., 2
Kevin Murphy, Yair Weiss, Michael Jordan (1999)
Loopy Belief Propagation for Approximate Inference: An Empirical Study
S. Lauritzen, D. Spiegelhalter (1990)
Local computations with probabilities on graphical structures and their application to expert systemsJournal of the royal statistical society series b-methodological, 50
S. Lauritzen, N. Wermuth (1989)
Graphical Models for Associations between Variables, some of which are Qualitative and some QuantitativeAnnals of Statistics, 17
D. Mauá, Cassio Campos, A. Benavoli, Alessandro Antonucci (2014)
Probabilistic Inference in Credal Networks: New Complexity ResultsJ. Artif. Intell. Res., 50
M. Chavira, Adnan Darwiche (2005)
Compiling Bayesian Networks with Local Structure
D. Chickering (2003)
Optimal Structure Identification With Greedy SearchJ. Mach. Learn. Res., 3
J. Pearl (2000)
Causality: Models, Reasoning and Inference
W. Spohn (1988)
Ordinal Conditional Functions: A Dynamic Theory of Epistemic States
M. Koivisto, K. Sood (2004)
Exact Bayesian Structure Discovery in Bayesian NetworksJ. Mach. Learn. Res., 5
D. Chickering, D. Heckerman (1996)
Efficient Approximations for the Marginal Likelihood of Incomplete Data Given a Bayesian Network
L. Zadeh (1999)
Fuzzy sets as a basis for a theory of possibilityFuzzy Sets and Systems, 100
(1996)
Autonomous control logic to guide unmanned underwater vehicle
Wray Buntine (1991)
Theory Refinement on Bayesian Networks
Prakash Shenoy (1989)
A valuation-based language for expert systemsInt. J. Approx. Reason., 3
I. Tsamardinos, Laura Brown, C. Aliferis (2006)
The max-min hill-climbing Bayesian network structure learning algorithmMachine Learning, 65
C. Chow, Chao-Ming Liu (1968)
Approximating discrete probability distributions with dependence treesIEEE Trans. Inf. Theory, 14
Brandon Malone, Changhe Yuan, E. Hansen, S. Bridges (2011)
Improving the Scalability of Optimal Bayesian Network Learning with External-Memory Frontier Breadth-First Branch and Bound SearchArXiv, abs/1202.3744
Ramon Sangüesa, J. Cabos, U. Cortés (1998)
Possibilistic conditional independence: A similarity-based measure and its application to causal network learningInt. J. Approx. Reason., 18
B. Yaghlane, K. Mellouli (2008)
Inference in directed evidential networks based on the transferable belief modelInt. J. Approx. Reason., 48
Stuart Staniford-Chen, James Hoagland, J. McAlerney (2002)
Practical Automated Detection of Stealthy PortscansJ. Comput. Secur., 10
C. Borgelt, R. Kruse (2003)
Learning possibilistic graphical models from dataIEEE Trans. Fuzzy Syst., 11
M. Koivisto (2006)
Advances in Exact Bayesian Structure Discovery in Bayesian NetworksArXiv, abs/1206.6828
A. Delaplace, T. Brouard, H. Cardot (2006)
Two evolutionary methods for learning Bayesian network structures2006 International Conference on Computational Intelligence and Security, 1
J. Pearl, Thomas Verma (1991)
A Theory of Inferred Causation
Jonas Vlasselaer, Wannes Meert, Guy Broeck, L. Raedt (2016)
Exploiting local and repeated structure in Dynamic Bayesian NetworksArtif. Intell., 232
D. Dubois, H. Prade (1990)
The logical view of conditioning and its application to possibility and evidence theoriesInt. J. Approx. Reason., 4
S. Arnborg, D. Corneil, A. Proskurowski (1987)
Complexity of finding embeddings in a k -treeSiam Journal on Algebraic and Discrete Methods, 8
C. Simon, P. Weber, Alexandre Evsukoff (2008)
Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysisReliab. Eng. Syst. Saf., 93
Chaohui Wang, N. Komodakis, N. Paragios (2013)
Markov Random Field modeling, inference & learning in computer vision & image understanding: A surveyComput. Vis. Image Underst., 117
Marco Zaffalon (2002)
The naive credal classifierJournal of Statistical Planning and Inference, 105
Christian Eichhorn, Matthias Fey, G. Kern-Isberner (2016)
CP- and OCF-networks - a comparisonFuzzy Sets Syst., 298
P. Weber, Gabriela Medina-Oliva, C. Simon, B. Iung (2012)
Overview on Bayesian networks applications for dependability, risk analysis and maintenance areasEng. Appl. Artif. Intell., 25
G. Schwarz (1978)
Estimating the Dimension of a ModelAnnals of Statistics, 6
Cédric Auliac, Florence d'Alché-Buc, V. Frouin (2007)
Learning Transcriptional Regulatory Networks with Evolutionary Algorithms Enhanced with Niching
Fabio Cozman (2000)
Credal networksArtif. Intell., 120
D. Dubois, H. Prade (1988)
Possibility Theory - An Approach to Computerized Processing of Uncertainty
J. Pearl (1986)
Fusion, Propagation, and Structuring in Belief NetworksProbabilistic and Causal Inference
G. Cooper (1990)
The Computational Complexity of Probabilistic Inference Using Bayesian Belief NetworksArtif. Intell., 42
G. Shafer, J. Pearl (1990)
Readings in Uncertain Reasoning
L. Zadeh, J. Kacprzyk (1992)
Fuzzy Logic for the Management of Uncertainty
F. Pernkopf, J. Bilmes (2005)
Discriminative versus generative parameter and structure learning of Bayesian network classifiersProceedings of the 22nd international conference on Machine learning
P. Larrañaga, M. Poza, Y. Yurramendi, R. Murga, Cindy Kuijpers (1996)
Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control ParametersIEEE Trans. Pattern Anal. Mach. Intell., 18
Marco Cristo, P. Calado, M. Silveira, I. Silva, R. Muntz, B. Ribeiro-Neto (2003)
Bayesian belief networks for IRInt. J. Approx. Reason., 34
P. Maher (1984)
Book Review:The Enterprise of Knowledge: An Essay on Knowledge, Credal Probability, and Chance Isaac LeviPhilosophy of Science
(1994)
Réseaux d'inférence pour le raisonnement possibiliste
M. Chickering, D. Geiger, D. Heckerman (1995)
Learning Bayesian Networks: Search Methods and Experimental Results
G. Shafer (2020)
A Mathematical Theory of EvidenceA Mathematical Theory of Evidence
Alessandro Antonucci, Ralph Brühlmann, Alberto Piatti, Marco Zaffalon (2009)
Credal networks for military identification problemsInt. J. Approx. Reason., 50
I. Zaarour, L. Heutte, Philippe Leray, J. Labiche, B. Eter, D. Mellier (2004)
Clustering And Bayesian Network Approaches For Discovering Handwriting Strategies Of Primary School ChildrenInt. J. Pattern Recognit. Artif. Intell., 18
D. Chickering (1995)
A Transformational Characterization of Equivalent Bayesian Network StructuresArXiv, abs/1302.4938
Ross Shachter (1986)
Evaluating Influence DiagramsOper. Res., 34
R. Robinson (1977)
Counting unlabeled acyclic digraphs
Sergio Morais, A. Aussem (2008)
A Novel Scalable and Data Efficient Feature Subset Selection Algorithm
Anicet Bart, F. Koriche, Jean-Marie Lagniez, P. Marquis (2016)
An Improved CNF Encoding Scheme for Probabilistic Inference
Jianbing Ma, Weiru Liu (2008)
A General Model for Epistemic State Revision using Plausibility Measures
N. Friedman, D. Geiger, M. Goldszmidt (1997)
Bayesian Network ClassifiersMachine Learning, 29
Tong Wang, Jie Yang (2010)
A heuristic method for learning Bayesian networks using discrete particle swarm optimizationKnowledge and Information Systems, 24
Christopher Meek (1995)
Causal inference and causal explanation with background knowledge
J. Muruzábal, C. Cotta (2007)
A Study on the Evolution of Bayesian Network Graph Structures
Cassio Campos (2011)
New Complexity Results for MAP in Bayesian Networks
G. Cooper, E. Herskovits (1992)
A Bayesian method for the induction of probabilistic networks from dataMachine Learning, 9
P. Smets, R. Kennes (1991)
The Transferable Belief Model
J. Tainter (2014)
What is Transformation
(1982)
Foundation for a possibility theory. Fuzzy information and decision processes
J. Peña, R. Nilsson, J. Björkegren, J. Tegnér (2007)
Towards scalable and data efficient learning of Markov boundariesInt. J. Approx. Reason., 45
H Akaike (1970)
203Ann Inst Stat Math, 22
Adnan Darwiche, J. Pearl (1994)
On the Logic of Iterated Belief RevisionArtif. Intell., 89
L. Zadeh (1975)
The concept of a linguistic variable and its application to approximate reasoning - IInf. Sci., 8
S. Benferhat, D. Dubois, Laurent Garcia, H. Prade (2002)
On the transformation between possibilistic logic bases and possibilistic causal networksInt. J. Approx. Reason., 29
Kevin Murphy, Stuart Russell (2002)
Dynamic bayesian networks: representation, inference and learning
Michael Jordan, Zoubin Ghahramani, T. Jaakkola, L. Saul (1999)
An Introduction to Variational Methods for Graphical ModelsMachine Learning, 37
R. Mourad, Christine Sinoquet, Philippe Leray (2011)
A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studiesBMC Bioinformatics, 12
S. Benferhat, Amélie Levray, Karim Tabia (2015)
Probability-Possibility Transformations: Application to Credal Networks
R. Greiner, Xiaoyuan Su, Bin Shen, Wei Zhou (2002)
Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net ClassifiersMachine Learning, 59
Maroua Haddad, Philippe Leray, N. Amor (2015)
Learning possibilistic networks from data: a survey
Joseph Halpern (2000)
Conditional Plausibility Measures and Bayesian NetworksJ. Artif. Intell. Res., 14
D. Koller, N. Friedman (2009)
Probabilistic Graphical Models - Principles and Techniques
D. Dubois, Giovanni Fusco, H. Prade, A. Tettamanzi (2017)
Uncertain logical gates in possibilistic networks: Theory and application to human geographyInt. J. Approx. Reason., 82
H. Akaike (1970)
Statistical predictor identificationAnnals of the Institute of Statistical Mathematics, 22
Vincent Auvray, L. Wehenkel (2002)
On the Construction of the Inclusion Boundary Neighbourhood for Markov Equivalence Classes of Bayesian Network StructuresArXiv, abs/1301.0553
N. Zhang, D. Poole (1994)
A simple approach to Bayesian network computations
Eamonn Keogh, M. Pazzani (1999)
Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches
J. Gebhardt, R. Kruse (1995)
Learning possibilistic networks from dataProceedings of 1995 IEEE International Conference on Fuzzy Systems., 3
D. Heckerman (1999)
A Tutorial on Learning with Bayesian Networks
A. Valdes, K. Skinner (2000)
Adaptive, Model-Based Monitoring for Cyber Attack Detection
(1996)
Efficient Approximation for the Marginal Like
D. Heckerman, Eric Horvitz, Bharat Nathwani (1992)
Toward Normative Expert Systems: Part I The Pathfinder ProjectMethods of Information in Medicine, 31
R. Bouckaert (1993)
Probalistic Network Construction Using the Minimum Description Length Principle
Alessandro Antonucci, Cassio Campos (2011)
Decision Making by Credal Nets2011 Third International Conference on Intelligent Human-Machine Systems and Cybernetics, 1
M. Henrion (1986)
Propagating uncertainty in bayesian networks by probabilistic logic sampling
Ye Du, Huiqiang Wang, Yonggang Pang (2004)
A hidden Markov models-based anomaly intrusion detection methodFifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788), 5
I. Tsamardinos, C. Aliferis, A. Statnikov (2003)
Time and sample efficient discovery of Markov blankets and direct causal relations
D. Geiger, Thomas Verma, J. Pearl (1990)
Identifying independence in bayesian networksNetworks, 20
(1996)
Oxford statistical science series
Christian Eichhorn, G. Kern-Isberner (2015)
Using inductive reasoning for completing OCF-networksJ. Appl. Log., 13
P. Parviainen, M. Koivisto (2009)
Exact Structure Discovery in Bayesian Networks with Less Space
R. Howard, J. Matheson (2005)
Influence DiagramsDecis. Anal., 2
K. Karplus, Sol Katzman, George Shackleford, Martina Koeva, Jenny Draper, Bret Barnes, Marcia Soriano, R. Hughey (2005)
SAM‐T04: What is new in protein–structure prediction for CASP6Proteins: Structure, 61
Hong Xu, P. Smets (1994)
Evidential Reasoning with Conditional Belief Functions
Prakash Shenoy (1993)
Valuation Networks and Conditional Independence
E. Horvitz, M. Barry (1995)
Display of Information for Time-Critical Decision MakingArXiv, abs/1302.4959
D. Grossman, Pedro Domingos (2004)
Learning Bayesian network classifiers by maximizing conditional likelihoodProceedings of the twenty-first international conference on Machine learning
M. Chavira, Adnan Darwiche, M. Jaeger (2006)
Compiling relational Bayesian networks for exact inferenceInt. J. Approx. Reason., 42
N. Amor, S. Benferhat (2005)
Graphoid Properties Of Qualitative Possibilistic Independence RelationsInt. J. Uncertain. Fuzziness Knowl. Based Syst., 13
Rónán Daly, Q. Shen, Stuart Aitken (2011)
Learning Bayesian networks: approaches and issuesThe Knowledge Engineering Review, 26
O. Pourret, Patrick. Naïm, B. Marcot (2008)
Bayesian networks : a practical guide to applications
P. Spirtes, C. Glymour, R. Scheines (1993)
Causation, prediction, and search
S. Benferhat, Amélie Levray, Karim Tabia (2015)
On the Analysis of Probability-Possibility Transformations: Changing Operations and Graphical Models
D. Heckerman, D. Geiger, D. Chickering (1994)
Learning Bayesian Networks: The Combination of Knowledge and Statistical DataMachine Learning, 20
J. Pearl (1982)
Reverend Bayes on Inference Engines: A Distributed Hierarchical ApproachProbabilistic and Causal Inference
William Harper (1983)
The Enterprise of Knowledge: An Essay on Knowledge, Credal Probability and Chance by Isaac LeviThe Journal of Philosophy, 80
Karim Tabia (2016)
Possibilistic Graphical Models for Uncertainty Modeling
D. Geiger, Thomas Verma, J. Pearl (2013)
d-Separation: From Theorems to Algorithms
S. Benferhat, Salma Smaoui (2005)
Hybrid possibilistic networksInt. J. Approx. Reason., 44
X. An, D. Jutla, N. Cercone (2006)
Privacy intrusion detection using dynamic Bayesian networks
Ross Shachter, D. Bhattacharjya (2011)
Solving Influence Diagrams: Exact Algorithms
J. Pearl (1991)
Probabilistic reasoning in intelligent systems - networks of plausible inference
W. Long (1989)
Medical Diagnosis using a probabilistic causal networkAppl. Artif. Intell., 3
P. Fonck (1997)
A comparative study of possibilistic conditional independence and lack of interactionInt. J. Approx. Reason., 16
Céline Fiot, G. Saptawati, Anne Laurent, M. Teisseire (2008)
Learning Bayesian Network Structure from Incomplete Data without Any Assumption
A. Biedermann, F. Taroni (2012)
Bayesian networks for evaluating forensic DNA profiling evidence: a review and guide to literature.Forensic science international. Genetics, 6 2
Sandeep Kumar, E. Spafford (1994)
An Application of Pattern Matching in Intrusion Detection
[Many real world problemsBenferhat, Salem and applications require to exploit incomplete, complex and uncertain information. BeliefLeray, Philippe graphical models encompass a wide range of graphical formalisms for representing and Reasoning reasoning with uncertain and complex information. They generallyTabia, Karim involve a graphical component which can be directed or undirected and a numerical one depending on the considered uncertainty setting. The graphical component encodes a set of independence statements while the numerical one quantifies the uncertainty regarding variables. The main use of belief graphical models is knowledge representation, reasoning and decision making for multivariate problems. Belief graphical models can be built either by eliciting the uncertain knowledge of an expert or automatically learnt from data using machine learning techniques. Many types of inference algorithms exist and many platforms are now available allowing modeling and reasoning with belief graphical models in many application areas such as diagnosis, forecasting, decision making and classification. This chapter provides an overview of the most common belief graphical models. In particular, it gives an overview on various aspects related to graphical models for uncertainty: representation, inference, learning and finally applications.]
Published: May 8, 2020
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.