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Generating Bayesian Networks from Probability Logic Knowledge Bases [chapter]

Peter Haddawy
1994 Uncertainty Proceedings 1994  
We present a method for dynamically gen erating Bayesian networks from knowledge bases consisting of first-order probability logic sentences.  ...  We present a subset of proba bility logic sufficient for representing the class of Bayesian networks with discrete-valued nodes.  ...  Acknowledgements I would like to thank Kate Fowler, Bob Krieger and Meliani Suwandi for many helpful discussions and for implementing the generation algorithm, and Susan McRoy for helpful comments on an  ... 
doi:10.1016/b978-1-55860-332-5.50038-9 fatcat:hdipjkfvlbdp3n32r3fg247eza

On the combination of logical and probabilistic models for information analysis

Jingsong Wang, John Byrnes, Marco Valtorta, Michael Huhns
2011 Applied intelligence (Boston)  
We define a formalism for the conversion of automatically generated natural deduction proof trees into Bayesian networks.  ...  Bayesian network tools represent probabilistic and causal information, but in the worst case scale as poorly as some formal logical systems and require specialized expertise to use effectively.  ...  In all the above approaches standard Bayesian networks are constructed from knowledge bases, a metaapproach called Knowledge Based Model Construction (KBMC).  ... 
doi:10.1007/s10489-010-0272-x fatcat:htgeqdq56nfrlpjcmwrebwiexy

Tractable Reasoning with Bayesian Description Logics [chapter]

Claudia d'Amato, Nicola Fanizzi, Thomas Lukasiewicz
2008 Lecture Notes in Computer Science  
In this paper, we present a probabilistic generalization of the DL-Lite description logics, which is based on Bayesian networks.  ...  We show that the new probabilistic description logics are rich enough to properly extend both the DL-Lite description logics as well as Bayesian networks.  ...  Summary and Outlook We have presented probabilistic generalizations of the DL-Lite description logics, which are based on Bayesian networks.  ... 
doi:10.1007/978-3-540-87993-0_13 fatcat:hjdc2bxdejfypb4r2zyg7yjcsq

Logical Bayesian Networks and Their Relation to Other Probabilistic Logical Models [chapter]

Daan Fierens, Hendrik Blockeel, Maurice Bruynooghe, Jan Ramon
2005 Lecture Notes in Computer Science  
PRMs, BLPs and LBNs all follow the principle of Knowledge Based Model Construction: they offer a language that can be used to specify general probabilistic logical knowledge and they provide a methodology  ...  We review Logical Bayesian Networks, a language for probabilistic logical modelling, and discuss its relation to Probabilistic Relational Models and Bayesian Logic Programs.  ...  Logical Bayesian Networks (LBNs) LBNs use a Logic Programming based language.  ... 
doi:10.1007/11536314_8 fatcat:x5dnancj55fs5nhenhtnsc4eoy

A Reasoner for Generalized Bayesian DL-Programs

Livia Predoiu
2008 International Semantic Web Conference  
In this paper, we describe an ongoing reasoner implementation for reasoning with generalized Bayesian dl-programs and thus for dealing with deterministic ontologies and logic programs and probabilistic  ...  A general Bayesian dl-program is a knowledge base KB = (L, P, µ, Comb) where L is the knowledge base corresponding to the union of the ontologies to be integrated.  ...  From the result of the meta reasoner, we can create a Bayesian network which can be dealt with with SamIam 3 .  ... 
dblp:conf/semweb/Predoiu08 fatcat:fgy2q3jnvzexzomsh53ixjbx7a

Comparing Student Model Accuracy with Bayesian Network and Fuzzy Logic in Predicting Student Knowledge Level

Muhammad Danaparamita, Ford Lumban Gaol
2014 International Journal of Multimedia and Ubiquitous Engineering  
Bayesian Network and Fuzzy Logic is the most widely used to develop student model.  ...  In this paper we will compare the accuracy of student model developed with Bayesian Network and Fuzzy Logic in predicting student knowledge level.  ...  Full joint probability distribution form all variable in Bayesian Network acquire from conditional probability each variable based on all variable's parent.  ... 
doi:10.14257/ijmue.2014.9.4.12 fatcat:qbii4x45brbcnkqk7ikzh6wo6m

Automated Reasoning for Relational Probabilistic Knowledge Representation [chapter]

Christoph Beierle, Marc Finthammer, Gabriele Kern-Isberner, Matthias Thimm
2010 Lecture Notes in Computer Science  
KReator is a toolbox for representing, learning, and automated reasoning with various approaches combining relational first-order logic with probabilities.  ...  Background Bayesian logic programming combines logic programming and Bayesian networks [3, Ch. 10] .  ...  Various extensions to a first-order setting like Bayesian logic programs [3, Ch. 10] or Markov logic networks [3, Ch. 12] have been proposed.  ... 
doi:10.1007/978-3-642-14203-1_19 fatcat:3ao4vsjlrvaptn3u26ainujgxe

Learning Terminological Naive Bayesian Classifiers under Different Assumptions on Missing Knowledge

Pasquale Minervini, Claudia d'Amato, Nicola Fanizzi
2011 International Semantic Web Conference  
We present a Statistical Relational Learning system designed for learning terminological naïve Bayesian classifiers, which estimate the probability that a generic individual belongs to the target concept  ...  given its membership to a set of Description Logic concepts.  ...  We presented a Statistical Relational Learning method designed for learning terminological naïve Bayesian classifiers, a ML method based on the naïve Bayes assumption for estimating the probability that  ... 
dblp:conf/semweb/MinervinidF11 fatcat:wppi7bxab5h73invz4kjgkzdnm

Evaluation and Comparison Criteria for Approaches to Probabilistic Relational Knowledge Representation [chapter]

Christoph Beierle, Marc Finthammer, Gabriele Kern-Isberner, Matthias Thimm
2011 Lecture Notes in Computer Science  
We discuss and illustrate the criteria thoroughly by applying them to several approaches to probabilistic relational knowledge representation, in particular, Bayesian logic programs, Markov logic networks  ...  , and three approaches based on the principle of maximum entropy.  ...  , Markov logic networks (MLN) [5] , or relational Bayesian networks [8] .  ... 
doi:10.1007/978-3-642-24455-1_6 fatcat:orp6j3hmj5btnovnt4czh436xa

Automated transformation of probabilistic knowledge for a medical diagnostic system

Y C Li, P J Haug, H R Warner
1994 Proceedings. Symposium on Computer Applications in Medical Care  
In this paper, we describe the knowledge representation currently used in Iliad and a probabilistic representation based on the Bayesian network formalism which can be derived using the information that  ...  the Iliad knowledge base contains.  ...  We can run this program multiple times with different values of this probability to generate Bayesian networks with different emphasis on "Other-causes".  ... 
pmid:7950028 pmcid:PMC2247732 fatcat:2jtjby4dpvdk5dxlcme56v7jnq

Special issue: Combining probability and logic

Jürgen Landes, Jon Williamson
2016 Journal of Applied Logic  
The papers in this volume concern either the special focus on the connection between probabilistic logic and probabilistic networks or the more general question of the links between probability and logic  ...  and funded by the Leverhulme Trust from 2006-8.  ...  The advantage of credal networks over Bayesian networks is their increased generality and flexibility, but the price for this is added computational complexity.  ... 
doi:10.1016/j.jal.2015.09.009 fatcat:37jxfr4tjrcinlk6vvlontftha

Special issue on Combining Probability and Logic

Jon Williamson, Dov Gabbay
2003 Journal of Applied Logic  
The papers in this volume concern either the special focus on the connection between probabilistic logic and probabilistic networks or the more general question of the links between probability and logic  ...  and funded by the Leverhulme Trust from 2006-8.  ...  The advantage of credal networks over Bayesian networks is their increased generality and flexibility, but the price for this is added computational complexity.  ... 
doi:10.1016/s1570-8683(03)00009-0 fatcat:ldvbd3jza5axlkp2x4isedswze

Risk Prediction for Production of an Enterprise

Kumar Ravi, Sheopujan Singh
2013 International Journal of Computer Applications Technology and Research  
Bayesian network provides the feature to represent the probabilistic uncertainty and reasoning about probabilistic knowledge base, which is used here to represent the probable risks behind each causes  ...  expressivity of first-order logic and probabilistic feature of Bayesian network.  ...  Paulo Cesar G. da Costa for giving permissions to use any contents from their Ph. D. theses and papers.  ... 
doi:10.7753/ijcatr0203.1006 fatcat:t7jh5olbijdnti6ocjvzibakwu

Combining Probability and Logic

Fabio Cozman, Rolf Haenni, Jan-Willem Romeijn, Federica Russo, Gregory Wheeler, Jon Williamson
2009 Journal of Applied Logic  
The papers in this volume concern either the special focus on the connection between probabilistic logic and probabilistic networks or the more general question of the links between probability and logic  ...  This volume arose out of an international, interdisciplinary academic network on Probabilistic Logic and Probabilistic Networks involving four of us (Haenni, Romeijn, Wheeler and Williamson), called Progicnet  ...  The advantage of credal networks over Bayesian networks is their increased generality and flexibility, but the price for this is added computational complexity.  ... 
doi:10.1016/j.jal.2007.12.001 fatcat:uosf3cmdlbbk5b3gxc4uqbxq7m

Using First-Order Probability Logic for the Construction of Bayesian Networks [article]

Fahiem Bacchus
2013 arXiv   pre-print
We present a mechanism for constructing graphical models, specifically Bayesian networks, from a knowledge base of general probabilistic information.  ...  These pieces are composed to generate a joint probability distribution specified as a Bayesian network.  ...  Conclusions and Future Work We have outlined a mechanism for KBMC of Bayesian networks from a knowledge base expressed in a first order probabilistic logic.  ... 
arXiv:1303.1480v1 fatcat:2fd76bqparhplhabz4ajnd5c54
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