@misc{Butz2003Comparing, title = {Comparing Hierarchical Markov Networks and Multiply Sectioned Bayesian Networks}, author = {C.J. Butz and H. Geng}, year = 2003, url = {citeseer.ist.psu.edu/669674.html}, description = {Comparing Hierarchical Markov Networks and Multiply Sectioned Bayesian Networks (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/2061e514f01016417c8f85ad474f29790/wnpxrz}, keywords = {av:online imported read bayesian proj:o4p} } @inproceedings{Butz2005Modelling, title = {Modelling multiagent Bayesian networks with inclusion dependencies}, author = {C.J. Butz and F. Fang}, booktitle = {Intelligent Agent Technology, IEEE/WIC/ACM International Conference on}, pages = {455- 458}, year = 2005, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1565582}, isbn = {0-7695-2416-8}, doi = {10.1109/IAT.2005.103}, description = {Welcome to IEEE Xplore 2.0: Modelling multiagent Bayesian networks with inclusion dependencies}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/26e94fac1d40810ad460beb2cad9706a8/wnpxrz}, keywords = {imported toread av:attached proj:o4p bn bayesian} } @misc{gasevic-approaching, title = {Approaching OWL and MDA through Technological Spaces}, author = {Dragan Gasevic and Dragan Djuric and Vladan Devedzic and Violeta Damjanovic}, year = 2004, url = {citeseer.ist.psu.edu/gasevic04approaching.html}, description = {Approaching OWL and MDA through Technological Spaces (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/227dd635a16c3ffb2fb2a8e1fb7553fbf/wnpxrz}, keywords = {toread proj:o4p mda imported ontology owl} } @article{keyhere, title = {Petri net ontology}, author = {Dragan Gasevic and Vladan Devedzic}, journal = {Knowledge-Based Systems}, month = {#aug#}, number = 4, pages = {220--234}, volume = 19, year = 2006, url = {http://www.sciencedirect.com/science/article/B6V0P-4J9MRFP-1/2/b96857ea5354d98896096c69370e208c}, description = {ScienceDirect - Knowledge-Based Systems : Petri net ontology}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/2989060334d7643a030e100db3fb91b9c/wnpxrz}, keywords = {ontology petri semanticweb proj:o4p toread net} } @article{Gasevic:8July2007:1476-1289:374, title = {Interoperable Petri net models via ontology}, author = {Dragan Gasevic and Vladan Devedzic}, journal = {International Journal of Web Engineering and Technology}, pages = {374-396(23)}, volume = 3, year = {8 July 2007}, url = {http://www.ingentaconnect.com/content/ind/ijwet/2007/00000003/00000004/art00002}, doi = {doi:10.1504/IJWET.2007.014439}, description = {IngentaConnect Interoperable Petri net models via ontology}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/29b1e7019fa77f0d81f4e68a952e88bcf/wnpxrz}, keywords = {imported proj:o4p net ontology toread petri} } @article{Wong:2001, title = {Constructing the dependency structure of a multiagent probabilisticnetwork}, author = {S.K.M. Wong and C.J. Butz}, booktitle = {Transactions on Knowledge and Data Engineering}, pages = {395-415}, volume = 13, year = 2001, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=929898}, issn = {1041-4347}, doi = {10.1109/69.929898}, description = {Welcome to IEEE Xplore 2.0: Constructing the dependency structure of a multiagent probabilisticnetwork}, abstract = {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 cover}, biburl = {http://www.bibsonomy.org/bibtex/2fc0e8e91bd2ba381fb79d135268e9e7e/wnpxrz}, keywords = {proj:o4p imported toread bayesian} } @misc{butz-critical, title = {Critical Remarks on Bayesian Network Libraries}, author = {C.J. Butz}, year = 2003, url = {citeseer.ist.psu.edu/butz02critical.html}, description = {Critical Remarks on Bayesian Network Libraries (ResearchIndex)}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/2906eea3966a85491a011de4ff1942a8e/wnpxrz}, keywords = {imported read proj:o4p bayesian} } @article{butz01axiomatizing, title = {On Axiomatizing Probabilistic Conditional Independencies in {Bayesian} Networks}, author = {C. J. Butz}, journal = {Lecture Notes in Computer Science}, pages = {131--??}, volume = 2198, year = 2001, url = {citeseer.ist.psu.edu/526216.html}, description = {On Axiomatizing Probabilistic Conditional Independencies in Bayesian Networks (ResearchIndex)}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/25d70845d10acb7931d721e8a6cde67d5/wnpxrz}, keywords = {imported proj:o4p read bayesian} } @article{Sadeghi:2005:Stud-Health-Technol-Inform:16160263, title = {Ontology Driven Construction of a Knowledgebase for Bayesian Decision Models Based on UMLS}, author = {S Sadeghi and A Barzi and J W Smith}, journal = {Stud Health Technol Inform}, pages = {223-228}, volume = 116, year = 2005, url = {http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=ShowDetailView&TermToSearch=16160263}, pmid = {16160263}, doi = {}, description = {Ontology Driven Construction of a Knowledgebase fo...[Stud Health Technol Inform. 2005] - PubMed Result}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/24ee8d31844bc59b16701fe1d0f7b9976/wnpxrz}, keywords = {imported bayesian noaccess proj:o4p ontology toread} } @techreport{heckerman95tutonlearnbayesnets, title = {A tutorial on learning with bayesian networks}, address = {Redmond, Washington}, author = {D. Heckerman}, institution = {Microsoft Research}, note = {Revised June 96}, year = 1995, url = {citeseer.ist.psu.edu/article/heckerman96tutorial.html}, key = {MSR-TR-95-06}, description = {A Tutorial on Learning With Bayesian Networks - Heckerman (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/2d28c962a3a1f5219a3c25b5f7371e13d/wnpxrz}, keywords = {imported proj:o4p toread bayesian} } @article{citeulike:515215, title = {The Information Geometry of Hierarchical Bayesian Models}, author = { Seon}, year = 2003, url = {http://www.di.univr.it/documenti/Avviso/all/all854137.ps}, id = {515215}, priority = {2}, description = {CiteULike: The Information Geometry of Hierarchical Bayesian Models}, biburl = {http://www.bibsonomy.org/bibtex/273fc37491ef4854060e4e59e69842de8/wnpxrz}, keywords = {av:online toread bayesian proj:o4p} } @misc{murphy-proposed, title = {Proposed design for {gR}, a graphical models toolkit for {R}}, author = {Kevin P. Murphy}, year = 2003, url = {citeseer.ist.psu.edu/murphy03proposed.html}, description = {Proposed design for gR, a graphical models toolkit for R (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/2de4c4027d6e1de6ebb25eeca7cdd2106/wnpxrz}, keywords = {software proj:o4p imported toread bayesian} } @misc{murphy01introduction, title = {An introduction to graphical models}, author = {K. Murphy}, year = 2001, url = {citeseer.ist.psu.edu/murphy01introduction.html}, description = {An Introduction to Graphical Models - Murphy (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/24826536cb1f9a6a7efba3c0291fa7431/wnpxrz}, keywords = {imported bayesian toread introduction proj:o4p} } @misc{and-hierarchical, title = {Hierarchical Bayesian Networks: an Approach}, author = {To Classification}, year = 2004, url = {citeseer.ist.psu.edu/743804.html}, description = {Hierarchical Bayesian Networks: an Approach (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/24dd4d98912315add99ce482156f327a2/wnpxrz}, keywords = {bayesian toread proj:o4p imported} } @misc{sutton03guided, title = {Guided incremental construction of belief networks}, author = {C. Sutton and B. Burns and C. Morrison and P. Cohen}, year = 2003, url = {citeseer.ist.psu.edu/article/sutton03guided.html}, description = {Guided Incremental Construction of Belief Networks - Sutton, Burns, Morrison, Cohen (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/24c46a6daa502fc0b18e770d9f79d1040/wnpxrz}, keywords = {bayesian read proj:o4p} } @misc{helsper-ontologies, title = {Ontologies for Probabilistic Networks}, author = {Eveline M. Helsper and Linda C. van der Gaag}, year = 2003, url = {citeseer.ist.psu.edu/helsper03ontologies.html}, description = {Ontologies for Probabilistic Networks (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/216d667c9feef75a33ebc64ad898e5aea/wnpxrz}, keywords = {toread bayesian proj:o4p ontology imported} } @misc{neil99building, title = {Building large-scale Bayesian Networks}, author = {M. Neil and L. Fenton}, year = 1999, url = {citeseer.ist.psu.edu/neil99building.html}, description = {Building Large-Scale Bayesian Networks - Neil, Fenton (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/2127f6234bd6a6f3475e8c3324a364099/wnpxrz}, keywords = {toread bayesian proj:o4p} } @inproceedings{citeulike:777791, title = {VIBES: A Variational Inference Engine for Bayesian Networks}, author = {Christopher M. Bishop and David Spiegelhalter and John Winn}, booktitle = {NIPS 2002}, year = 2002, url = {http://scholar.google.fi/url?sa=U\&\#38;q=http://books.nips.cc/papers/files/nips15/AA37.pdf}, id = {777791}, priority = {4}, description = {CiteULike: VIBES: A Variational Inference Engine for Bayesian Networks}, biburl = {http://www.bibsonomy.org/bibtex/225706aefd4f46ec82bb187f3148f3754/wnpxrz}, keywords = {bayesian proj:o4p toread software} } @article{599385, title = {WinBUGS \– A Bayesian modelling framework: Concepts, structure, and extensibility}, address = {Hingham, MA, USA}, author = {David J. Lunn and Andrew Thomas and Nicky Best and David Spiegelhalter}, journal = {Statistics and Computing}, number = 4, pages = {325--337}, publisher = {Kluwer Academic Publishers}, volume = 10, year = 2000, url = {http://portal.acm.org/citation.cfm?id=599385}, issn = {0960-3174}, doi = {http://dx.doi.org/10.1023/A:1008929526011}, description = {WinBUGS – A Bayesian modelling framework}, biburl = {http://www.bibsonomy.org/bibtex/2f4660c1e1ce10152fb234bfb5a7aa763/wnpxrz}, keywords = {proj:o4p imported toread software bayesian} } @article{citeulike:777801, title = {Relational Dynamic Bayesian Networks}, author = {Sumit Sanghai and Pedro Domingos and Daniel Weld}, journal = {Journal of Artificial Intelligence Research}, pages = {759--797}, volume = 24, year = 2005, url = {http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume24/sanghai05a.pdf}, id = {777801}, priority = {4}, description = {CiteULike: Relational Dynamic Bayesian Networks}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/28b34c18e7a6193030ab8d0a95b907faa/wnpxrz}, keywords = {toread relational proj:o4p bayesian} }