http://www.bibsonomy.org/burst/concept/user/wnpxrz/proj:etBibSonomy BuRST Feed for /concept/user/wnpxrz/proj:et2008-07-27T03:44:57+02:00Comparing Hierarchical Markov Networks and Multiply Sectioned Bayesian Networks (ResearchIndex)http://www.bibsonomy.org/bibtex/2061e514f01016417c8f85ad474f29790/wnpxrzwnpxrz2008-01-29T17:29:22+01:00proj:o4p bayesian av:online imported read <span style="color:#555555;">C.J. <a href="http://www.bibsonomy.org/author/Butz">Butz</a> and H. <a href="http://www.bibsonomy.org/author/Geng">Geng</a> </span>(<em>2003</em>)Tue Jan 29 17:29:22 CET 2008Comparing Hierarchical Markov Networks and Multiply Sectioned Bayesian Networks2003proj:o4p bayesian av:online imported read Welcome to IEEE Xplore 2.0: Modelling multiagent Bayesian networks with inclusion dependencieshttp://www.bibsonomy.org/bibtex/26e94fac1d40810ad460beb2cad9706a8/wnpxrzwnpxrz2008-01-29T17:16:28+01:00av:attached imported bayesian proj:o4p toread bn <span style="color:#555555;">C.J. <a href="http://www.bibsonomy.org/author/Butz">Butz</a> and F. <a href="http://www.bibsonomy.org/author/Fang">Fang</a> </span><em>Intelligent Agent Technology, IEEE/WIC/ACM International Conference on, </em><em>page455- 458. </em>(<em>2005</em>)Tue Jan 29 17:16:28 CET 2008Intelligent Agent Technology, IEEE/WIC/ACM International Conference on455- 458Modelling multiagent Bayesian networks with inclusion dependencies2005av:attached imported bayesian proj:o4p toread bn Multiagent Bayesian networks (MABNs) are a powerful new framework for uncertainty management in a distributed environment. In a MABN, a collective joint probability distribution is defined by the conditional probability tables (CPTs) supplied by the individual agents. It is assumed, however, that CPTs supplied by individual agents agree on the variable domains, an assumption that does not necessarily hold in practice. In this paper, we suggest modelling MABNs with inclusion dependencies. Our approach is more flexible, and perhaps realistic, by allowing CPTs supplied by different agents to disagree on variable domains. Our main result is that the input CPTs define a joint probability distribution if and only if certain inclusion dependencies are satisfied. Other advantages, both practical and theoretical, of modelling MABNs with inclusion dependencies are discussed.Approaching OWL and MDA through Technological Spaces (ResearchIndex)http://www.bibsonomy.org/bibtex/227dd635a16c3ffb2fb2a8e1fb7553fbf/wnpxrzwnpxrz2008-01-27T20:40:04+01:00ontology proj:o4p mda toread owl imported <span style="color:#555555;">Dragan <a href="http://www.bibsonomy.org/author/Gasevic">Gasevic</a> and Dragan <a href="http://www.bibsonomy.org/author/Djuric">Djuric</a> and Vladan <a href="http://www.bibsonomy.org/author/Devedzic">Devedzic</a> and Violeta <a href="http://www.bibsonomy.org/author/Damjanovic">Damjanovic</a> </span>(<em>2004</em>)Sun Jan 27 20:40:04 CET 2008Approaching OWL and MDA through Technological Spaces2004ontology proj:o4p mda toread owl imported ScienceDirect - Knowledge-Based Systems : Petri net ontologyhttp://www.bibsonomy.org/bibtex/2989060334d7643a030e100db3fb91b9c/wnpxrzwnpxrz2008-01-27T20:36:36+01:00toread ontology petri semanticweb net proj:o4p <span style="color:#555555;">Dragan <a href="http://www.bibsonomy.org/author/Gasevic">Gasevic</a> and Vladan <a href="http://www.bibsonomy.org/author/Devedzic">Devedzic</a> </span><em>Knowledge-Based Systems</em><em>19(4):220--234</em><em>#aug#2006. </em>Sun Jan 27 20:36:36 CET 2008Knowledge-Based Systems#aug#4220--234Petri net ontology192006toread ontology petri semanticweb net proj:o4p The paper presents the Petri net ontology that enables sharing Petri nets on the Semantic Web. Previous work on formal methods for representing Petri nets mainly defines tool-specific descriptions or formats for model interchange. However, such efforts do not provide a suitable description for using Petri nets on the Semantic Web. This paper uses the Petri net UML model as a starting point for implementing the ontology. Resulting Petri net models are represented on the Semantic Web using XML-based ontology languages, RDF and OWL. We implemented a Petri net tool, P3, which can be used as a knowledge acquisition tool based on the Petri net ontology.IngentaConnect Interoperable Petri net models via ontologyhttp://www.bibsonomy.org/bibtex/29b1e7019fa77f0d81f4e68a952e88bcf/wnpxrzwnpxrz2008-01-27T20:35:41+01:00ontology net proj:o4p petri imported toread <span style="color:#555555;">Dragan <a href="http://www.bibsonomy.org/author/Gasevic">Gasevic</a> and Vladan <a href="http://www.bibsonomy.org/author/Devedzic">Devedzic</a> </span><em>International Journal of Web Engineering and Technology</em>(<em>8 July 2007</em>)Sun Jan 27 20:35:41 CET 2008International Journal of Web Engineering and Technology374-396(23)Interoperable Petri net models via ontology38 July 2007ontology net proj:o4p petri imported toread The paper presents a Petri net infrastructure that should allow sharing Petri nets on the Semantic Web. Previous solutions only provide model interchange mechanisms between Petri net tools. The Petri net ontology is a central part of our solution. The ontology is closely related to the Petri Net Markup Language PNML an ongoing Petri net community sharing effort. We developed the Petri net ontology using both UML and the Protege tool, whereas we use RDF and OWL to represent the ontology. We implemented a Petri net software tool P3 that can be used to convert the Petri net ontology compliant models to the formats of current Petri net tools e.g., DaNAMiCS, Petri Net Kernel, PIPE using eXtensible Stylesheet Language Transformations XSLT. In order to show how the ontology can be used, we developed a simple educational web application that uses RDF-annotated ontology-based Petri net learning materials.Welcome to IEEE Xplore 2.0: Constructing the dependency structure of a multiagent probabilisticnetworkhttp://www.bibsonomy.org/bibtex/2fc0e8e91bd2ba381fb79d135268e9e7e/wnpxrzwnpxrz2008-01-12T14:30:49+01:00imported bayesian proj:o4p toread <span style="color:#555555;">S.K.M. <a href="http://www.bibsonomy.org/author/Wong">Wong</a> and C.J. <a href="http://www.bibsonomy.org/author/Butz">Butz</a> </span>(<em>2001</em>)Sat Jan 12 14:30:49 CET 2008Transactions on Knowledge and Data Engineering395-415Constructing the dependency structure of a multiagent probabilisticnetwork132001imported bayesian proj:o4p toread A probabilistic network consists of a dependency structure and corresponding probability tables. The dependency structure is a graphical representation of the conditional independencies that are known to hold in the problem domain. We propose an automated process for constructing the combined dependency structure of a multiagent probabilistic network. Each domain expert supplies any known conditional independency information and not necessarily an explicit dependency structure. Our method determines a succinct representation of all the supplied independency information called a minimal cover. This process involves detecting all inconsistent information and removing all redundant information. A unique dependency structure of the multiagent probabilistic network can be constructed directly from this minimal cover. The main result is that the constructed dependency structure is a perfect-map of the minimal cover. That is, every probabilistic conditional independency logically implied by the minimal cover can be inferred from the dependency structure and every probabilistic conditional independency inferred from the dependency structure is logically implied by the minimal coverCritical Remarks on Bayesian Network Libraries (ResearchIndex)http://www.bibsonomy.org/bibtex/2906eea3966a85491a011de4ff1942a8e/wnpxrzwnpxrz2008-01-12T14:16:52+01:00read proj:o4p bayesian imported <span style="color:#555555;">C.J. <a href="http://www.bibsonomy.org/author/Butz">Butz</a> </span>(<em>2003</em>)Sat Jan 12 14:16:52 CET 2008Critical Remarks on Bayesian Network Libraries2003read proj:o4p bayesian imported Designing a large Bayesian network (BN) has been regarded as a difficult process. It has been suggested that BN libraries can be used to facilitate the construction of a large BN. That is, a large BN can be defined in terms of smaller BNs stored in a library. In this paper, we point out that it may be possible to combine the conditional independencies defined by the smaller BNs, but not the smaller BNs themselves.On Axiomatizing Probabilistic Conditional Independencies in Bayesian Networks (ResearchIndex)http://www.bibsonomy.org/bibtex/25d70845d10acb7931d721e8a6cde67d5/wnpxrzwnpxrz2008-01-12T13:53:34+01:00bayesian read proj:o4p imported <span style="color:#555555;">C. J. <a href="http://www.bibsonomy.org/author/Butz">Butz</a> </span><em>Lecture Notes in Computer Science</em>(<em>2001</em>)Sat Jan 12 13:53:34 CET 2008Lecture Notes in Computer Science131--??On Axiomatizing Probabilistic Conditional Independencies in {Bayesian} Networks21982001bayesian read proj:o4p imported Several researchers have suggested that Bayesian networks (BNs) should be used to manage the inherent uncertainty in information retrieval. However, it has been argued that manually constructing a large BN is a difficult process. In this paper, we obtain the only minimal complete subset of the semi-graphoid axiomatization governing the independency information in a BN. This result may be useful in developing an automated BN construction procedure for information retrieval purposes.Ontology Driven Construction of a Knowledgebase fo...[Stud Health Technol Inform. 2005] - PubMed Resulthttp://www.bibsonomy.org/bibtex/24ee8d31844bc59b16701fe1d0f7b9976/wnpxrzwnpxrz2008-01-12T12:52:02+01:00proj:o4p toread imported ontology bayesian noaccess <span style="color:#555555;">S <a href="http://www.bibsonomy.org/author/Sadeghi">Sadeghi</a> and A <a href="http://www.bibsonomy.org/author/Barzi">Barzi</a> and J W <a href="http://www.bibsonomy.org/author/Smith">Smith</a> </span><em>Stud Health Technol Inform</em>(<em>2005</em>)Sat Jan 12 12:52:02 CET 2008Stud Health Technol Inform223-228Ontology Driven Construction of a Knowledgebase for Bayesian Decision Models Based on UMLS1162005proj:o4p toread imported ontology bayesian noaccess All decision models use some form of language to describe domain elements and their interactions. The terminology is often specific and even unique to the algorithm and is a choice of designers. Nevertheless the domain elements and concepts of any decision problem are almost never unique and are used and reused in many other decision problems. The same is true about the information about those elements in the context of different decision problems. Put together, the information about any given element forms our knowledge about the element and if stored properly in a knowledgebase, can be used and reused as necessary without the need for duplication.In this paper we discuss creation of an ontology using UMLS vocabulary and semantic network that provides an abstract understanding of elements (or objects) in the problem domain. Based on this ontology, a knowledgebase will be constructed that provides further information about the object in relation to another object or objects as described in the semantic links.A knowledgebase structured as such will have the benefit of problem-independence. It can be expanded as needed to include other objects that are used in a different series of problems and therefore, will have a one to many mapping between knowledgebase and decision models. Updating the knowledgebase will update the decision models seamlessly and maintenance will be less of an issue across decision models and within the knowledgebase. We are using this approach in building Bayesian decision models using Bayesian networks; however, this approach is not limited to Bayesian networks and has been and can be used for other decision making purposes.A Tutorial on Learning With Bayesian Networks - Heckerman (ResearchIndex)http://www.bibsonomy.org/bibtex/2d28c962a3a1f5219a3c25b5f7371e13d/wnpxrzwnpxrz2008-01-12T12:51:31+01:00imported bayesian proj:o4p toread <span style="color:#555555;">D. <a href="http://www.bibsonomy.org/author/Heckerman">Heckerman</a> </span><em>Microsoft Research, </em><em>Redmond, Washington, </em>(<em>1995</em>)Sat Jan 12 12:51:31 CET 2008Redmond, WashingtonMSR-TR-95-06Revised June 96A tutorial on learning with bayesian networks1995imported bayesian proj:o4p toread CiteULike: The Information Geometry of Hierarchical Bayesian Modelshttp://www.bibsonomy.org/bibtex/273fc37491ef4854060e4e59e69842de8/wnpxrzwnpxrz2008-01-12T12:50:56+01:00bayesian proj:o4p toread av:online <span style="color:#555555;"> <a href="http://www.bibsonomy.org/author/Seon">Seon</a> </span>(<em>2003</em>)Sat Jan 12 12:50:56 CET 2008The Information Geometry of Hierarchical Bayesian Models2003bayesian proj:o4p toread av:online Proposed design for gR, a graphical models toolkit for R (ResearchIndex)http://www.bibsonomy.org/bibtex/2de4c4027d6e1de6ebb25eeca7cdd2106/wnpxrzwnpxrz2008-01-12T12:50:16+01:00proj:o4p imported software toread bayesian <span style="color:#555555;">Kevin P. <a href="http://www.bibsonomy.org/author/Murphy">Murphy</a> </span>(<em>2003</em>)Sat Jan 12 12:50:16 CET 2008Proposed design for {gR}, a graphical models toolkit for {R}2003proj:o4p imported software toread bayesian An Introduction to Graphical Models - Murphy (ResearchIndex)http://www.bibsonomy.org/bibtex/24826536cb1f9a6a7efba3c0291fa7431/wnpxrzwnpxrz2008-01-12T12:49:28+01:00proj:o4p imported toread bayesian introduction <span style="color:#555555;">K. <a href="http://www.bibsonomy.org/author/Murphy">Murphy</a> </span>(<em>2001</em>)Sat Jan 12 12:49:28 CET 2008An introduction to graphical models2001proj:o4p imported toread bayesian introduction Hierarchical Bayesian Networks: an Approach (ResearchIndex)http://www.bibsonomy.org/bibtex/24dd4d98912315add99ce482156f327a2/wnpxrzwnpxrz2008-01-12T12:48:51+01:00toread bayesian imported proj:o4p <span style="color:#555555;">To <a href="http://www.bibsonomy.org/author/Classification">Classification</a> </span>(<em>2004</em>)Sat Jan 12 12:48:51 CET 2008Hierarchical Bayesian Networks: an Approach2004toread bayesian imported proj:o4p Guided Incremental Construction of Belief Networks - Sutton, Burns, Morrison, Cohen (ResearchIndex)http://www.bibsonomy.org/bibtex/24c46a6daa502fc0b18e770d9f79d1040/wnpxrzwnpxrz2008-01-12T12:44:17+01:00proj:o4p read bayesian <span style="color:#555555;">C. <a href="http://www.bibsonomy.org/author/Sutton">Sutton</a> and B. <a href="http://www.bibsonomy.org/author/Burns">Burns</a> and C. <a href="http://www.bibsonomy.org/author/Morrison">Morrison</a> and P. <a href="http://www.bibsonomy.org/author/Cohen">Cohen</a> </span>(<em>2003</em>)Sat Jan 12 12:44:17 CET 2008Guided incremental construction of belief networks2003proj:o4p read bayesian Ontologies for Probabilistic Networks (ResearchIndex)http://www.bibsonomy.org/bibtex/216d667c9feef75a33ebc64ad898e5aea/wnpxrzwnpxrz2008-01-12T12:43:00+01:00bayesian proj:o4p toread ontology imported <span style="color:#555555;">Eveline M. <a href="http://www.bibsonomy.org/author/Helsper">Helsper</a> and Linda C. <a href="http://www.bibsonomy.org/author/van der Gaag">van der Gaag</a> </span>(<em>2003</em>)Sat Jan 12 12:43:00 CET 2008Ontologies for Probabilistic Networks2003bayesian proj:o4p toread ontology imported Building Large-Scale Bayesian Networks - Neil, Fenton (ResearchIndex)http://www.bibsonomy.org/bibtex/2127f6234bd6a6f3475e8c3324a364099/wnpxrzwnpxrz2008-01-12T12:42:26+01:00proj:o4p toread bayesian <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Neil">Neil</a> and L. <a href="http://www.bibsonomy.org/author/Fenton">Fenton</a> </span>(<em>1999</em>)Sat Jan 12 12:42:26 CET 2008Building large-scale Bayesian Networks1999proj:o4p toread bayesian CiteULike: VIBES: A Variational Inference Engine for Bayesian Networkshttp://www.bibsonomy.org/bibtex/225706aefd4f46ec82bb187f3148f3754/wnpxrzwnpxrz2008-01-12T12:41:54+01:00toread software proj:o4p bayesian <span style="color:#555555;">Christopher M. <a href="http://www.bibsonomy.org/author/Bishop">Bishop</a> and David <a href="http://www.bibsonomy.org/author/Spiegelhalter">Spiegelhalter</a> and John <a href="http://www.bibsonomy.org/author/Winn">Winn</a> </span><em>NIPS 2002, </em>(<em>2002</em>)Sat Jan 12 12:41:54 CET 2008NIPS 2002VIBES: A Variational Inference Engine for Bayesian Networks2002toread software proj:o4p bayesian WinBUGS – A Bayesian modelling frameworkhttp://www.bibsonomy.org/bibtex/2f4660c1e1ce10152fb234bfb5a7aa763/wnpxrzwnpxrz2008-01-12T12:40:42+01:00proj:o4p bayesian imported software toread <span style="color:#555555;">David J. <a href="http://www.bibsonomy.org/author/Lunn">Lunn</a> and Andrew <a href="http://www.bibsonomy.org/author/Thomas">Thomas</a> and Nicky <a href="http://www.bibsonomy.org/author/Best">Best</a> and David <a href="http://www.bibsonomy.org/author/Spiegelhalter">Spiegelhalter</a> </span><em>Statistics and Computing</em><em>10(4):325--337</em>(<em>2000</em>)Sat Jan 12 12:40:42 CET 2008Hingham, MA, USAStatistics and Computing4325--337WinBUGS \– A Bayesian modelling framework: Concepts, structure, and extensibility102000proj:o4p bayesian imported software toread CiteULike: Relational Dynamic Bayesian Networkshttp://www.bibsonomy.org/bibtex/28b34c18e7a6193030ab8d0a95b907faa/wnpxrzwnpxrz2008-01-12T12:39:53+01:00toread relational proj:o4p bayesian <span style="color:#555555;">Sumit <a href="http://www.bibsonomy.org/author/Sanghai">Sanghai</a> and Pedro <a href="http://www.bibsonomy.org/author/Domingos">Domingos</a> and Daniel <a href="http://www.bibsonomy.org/author/Weld">Weld</a> </span><em>Journal of Artificial Intelligence Research</em>(<em>2005</em>)Sat Jan 12 12:39:53 CET 2008Journal of Artificial Intelligence Research759--797Relational Dynamic Bayesian Networks242005toread relational proj:o4p bayesian Stochastic processes that involve the creation of objects and relations over time are widespread, but relatively poorly studied. For example, accurate fault diagnosis in factory assembly processes requires inferring the probabilities of erroneous assembly operations, but doing this efciently and accurately is difcult. Modeled as dynamic Bayesian networks, these processes have discrete variables with very large domains and extremely high dimensionality. In this paper, we introduce relational dynamic Bayesian networks (RDBNs), which are an extension of dynamic Bayesian networks (DBNs) to rst-order logic. RDBNs are a generalization of dynamic probabilistic relational models (DPRMs), which we had proposed in our previous work to model dynamic uncertain domains. We rst extend the Rao-Blackwellised particle ltering described in our earlier work to RDBNs. Next, we lift the assumptions associated with Rao-Blackwellization in RDBNs and propose two new forms of particle ltering. The rst one uses abstraction hierarchies over the predicates to smooth the particle lter's estimates. The second employs kernel density estimation with a kernel function specically designed for relational domains. Experiments show these two methods greatly outperform standard particle ltering on the task of assembly plan execution monitoring.