@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 = {bn bayesian av:attached proj:o4p toread imported} } @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 = {bayesian proj:o4p toread imported} } @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 = {bayesian proj:o4p toread ontology imported noaccess} } @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 = {bayesian proj:o4p toread imported} } @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 bayesian proj:o4p toread} } @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 bayesian proj:o4p toread imported} } @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 = {bayesian proj:o4p toread introduction imported} } @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 proj:o4p toread imported} } @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 = {bayesian proj:o4p toread 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 = {bayesian proj:o4p toread} } @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 = {software bayesian proj:o4p toread} } @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 = {software bayesian proj:o4p toread imported} } @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 = {bayesian relational proj:o4p toread} } @book{Edwards2000, title = {Introduction to Graphical Modelling}, author = {David Edwards}, howpublished = {Hardcover}, month = {June}, publisher = {Springer}, year = 2000, url = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20\&path=ASIN/0387950540}, id = {1041555}, priority = {2}, isbn = {0387950540}, description = {CiteULike: Introduction to Graphical Modelling}, abstract = {Graphic modelling is a form of multivariate analysis that uses graphs to represent models. These graphs display the structure of dependencies, both associational and causal, between the variables in the model. This textbook provides an introduction to graphical modelling with emphasis on applications and practicalities rather than on a formal development. It is based on the popular software package for graphical modelling, MIM, a freeware version of which can be downloaded from the Internet. Following an introductory chapter which sets the scene and describes some of the basic ideas of graphical modelling, subsequent chapters describe particular families of models, including log-linear models, Gaussian models, and models for mixed discrete and continuous variables. Further chapters cover hypothesis testing and model selection. Chapters 7 and 8 are new to the second edition. Chapter 7 describes the use of directed graphs, chain graphs, and other graphs. Chapter 8 summarizes some recent work on causal inference, relevant when graphical models are given a causal interpretation. This book will provide a useful introduction to this topic for students and researchers.}, biburl = {http://www.bibsonomy.org/bibtex/297597b0f59e5c6a53ad4df240bbda32c/wnpxrz}, keywords = {to:borrow bayesian book proj:o4p toread} } @article{505743, title = {An introduction to hidden Markov models and Bayesian networks}, address = {River Edge, NJ, USA}, author = {Zoubin Ghahramani}, pages = {9--42}, publisher = {World Scientific Publishing Co., Inc.}, year = 2002, url = {http://portal.acm.org/citation.cfm?id=505743}, isbn = {981-02-4564-5}, book = {Hidden Markov models: applications in computer vision}, description = {An introduction to hidden Markov models and Bayesian networks}, abstract = {We provide a tutorial on learning and inference in hidden Markov models in the context of the recent literature on Bayesian networks. This perspective make sit possible to consider novel generalizations to hidden Markov models with multiple hidden state variables, multiscale representations, and mixed discrete and continuous variables. Although exact inference in these generalizations is usually intractable, one can use approximate inference in these generalizations is usually intractable, one can use approximate inference algorithms such as Markov chain sampling and variational methods. We describe how such methods are applied to these generalized hidden Markov models. We conclude this review with a discussion of Bayesian methods for model selection in generalized HMMs.}, biburl = {http://www.bibsonomy.org/bibtex/2160056e0d5592179ad38b883c5314552/wnpxrz}, keywords = {bayesian hmm proj:o4p toread imported} } @article{DarrenJ._Wilkinson03012007, title = {{Bayesian methods in bioinformatics and computational systems biology}}, author = {Darren J. Wilkinson}, journal = {Brief Bioinform}, number = 2, pages = {109-116}, volume = 8, year = 2007, url = {http://bib.oxfordjournals.org/cgi/content/abstract/8/2/109}, doi = {10.1093/bib/bbm007}, eprint = {http://bib.oxfordjournals.org/cgi/reprint/8/2/109.pdf}, description = {Bayesian methods in bioinformatics and computational systems biology -- Wilkinson 8 (2): 109 -- Briefings in Bioinformatics}, abstract = {Bayesian methods are valuable, inter alia, whenever there is a need to extract information from data that are uncertain or subject to any kind of error or noise (including measurement error and experimental error, as well as noise or random variation intrinsic to the process of interest). Bayesian methods offer a number of advantages over more conventional statistical techniques that make them particularly appropriate for complex data. It is therefore no surprise that Bayesian methods are becoming more widely used in the fields of genetics, genomics, bioinformatics and computational systems biology, where making sense of complex noisy data is the norm. This review provides an introduction to the growing literature in this area, with particular emphasis on recent developments in Bayesian bioinformatics relevant to computational systems biology. }, biburl = {http://www.bibsonomy.org/bibtex/297cacebfa75cfaadcbfec9b2fd4e57fe/wnpxrz}, keywords = {bio bayesian proj:o4p toread imported} } @misc{druzdzel95building, title = {Building probabilistic networks: where do the numbers come from}, author = {M. Druzdzel and L. van der Gaag and M. Henrion and F. Jensen}, year = 1995, url = {citeseer.ist.psu.edu/article/druzdzel95building.html}, description = {Building Probabilistic Networks: Where Do the Numbers Come From? - a Guide to the Literature - Druzdzel, van der Gaag, Henrion, Jensen (ResearchIndex)}, biburl = {http://www.bibsonomy.org/bibtex/2b7eebe98f7b1778bf872074a8aaf2d79/wnpxrz}, keywords = {bayesian proj:o4p toread imported} } @article{citeulike:1612429, title = {A Primer on Learning in Bayesian Networks for Computational Biology}, author = {Chris J. Needham and James R. Bradford and Andrew J. Bulpitt and David R. Westhead}, journal = {PLoS Computational Biology}, month = {August}, number = 8, pages = {e129+}, volume = 3, year = 2007, url = {http://dx.doi.org/10.1371/journal.pcbi.0030129}, id = {1612429}, priority = {3}, doi = {10.1371/journal.pcbi.0030129}, description = {CiteULike: A Primer on Learning in Bayesian Networks for Computational Biology}, biburl = {http://www.bibsonomy.org/bibtex/21dc28e845cb66e9ce61858c1b06c9c0c/wnpxrz}, keywords = {bio bayesian proj:o4p toread} } @phdthesis{BayesOWL_A_Probabilistic_Framework_for_Semantic_Web, title = {{BayesOWL: A Probabilistic Framework for Semantic Web}}, author = {Zhongli Ding}, month = {December}, organization = {Computer Science and Electrical Engineering}, pages = 168, school = {University of Maryland, Baltimore County}, year = 2005, biburl = {http://www.bibsonomy.org/bibtex/204cc521a142eaf541c1a8521b35ed4aa/wnpxrz}, keywords = {bayesian owl proj:o4p toread ontology bayesowl imported} } @article{citeulike:562662, title = {Operations for Learning with Graphical Models}, author = {Wray L. Buntine}, journal = {Journal of Artificial Intelligence Research}, pages = {159--225}, volume = 2, year = 1994, url = {http://citeseer.ist.psu.edu/6938.html}, id = {562662}, priority = {0}, abstract = {This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition,...}, biburl = {http://www.bibsonomy.org/bibtex/28952cf0d215116e038971f7c30d6d19d/wnpxrz}, keywords = {model bayesian graphical proj:o4p learning toread ml} }