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Filtering by: Subject Bayesian statistical decision theory Remove constraint Subject: Bayesian statistical decision theory Decade 2000-2009 Remove constraint Decade: 2000-2009

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  • Analysis of Bayesian anytime inference algorithms

    Creator:
    Burgess, Scott Alan
    Abstract:
    This dissertation explores and analyzes the performance of several Bayesian anytime inference algorithms for dynamic influence diagrams. These algorithms are compared on the On-Line Maintenance Agent testbed, a software artifact permitting comparison of dynamic reasoning algorithms used by an agent on a variety of simulated maintenance and monitoring tasks. Analysis...
    Resource Type:
    Dissertation
    Full Text:
    specific probability queries in Bayesian networks, algorithms for answering decision queries in influence

  • Combining probabilistic socio-economic and biological information by Bayesian networks - fisheries and environmental experiences

    Creator:
    Uusitalo, Laura, Mäntyniemi, Samu, and Haapasaari, Päivi
    Abstract:
    In this paper, we review our experiences in combining cross disciplinary, probabilistic information by Bayesian networks. They seem to be potential tools in combining the optimization orientated economic modeling tradition to the somewhat different traditions of sociological sciences and further to the empirically orientated biological approaches. Calculus is based on...
    Resource Type:
    Other
    Full Text:
    socio-economic and biological information by Bayesian networks - fisheries and environmental

  • Constraining Bayesian network learning with qualitative models

    Creator:
    Altendorf, Eric E.
    Abstract:
    Machine learning encompasses probabilistic and statistical techniques that can build models from large quantities of extensional information (examples) with minimal dependence on intensional information (domain knowledge). This focus of machine learning is reflected in the never-ending quest for "off-the-shelf" classifiers. To generalize to unseen data, however, we must make use...
    Resource Type:
    Masters Thesis
    Full Text:
    qualitative statements impose on a statistical model, in par- ticular, Bayesian networks. We will also

  • Logic sampling, likelihood weighting and AIS-BN : an exploration of importance sampling

    Creator:
    Wang, Haiou
    Abstract:
    Logic Sampling, Likelihood Weighting and AIS-BN are three variants of stochastic sampling, one class of approximate inference for Bayesian networks. We summarize the ideas underlying each algorithm and the relationship among them. The results from a set of empirical experiments comparing Logic Sampling, Likelihood Weighting and AIS-BN are presented. We...
    Resource Type:
    Masters Thesis
    Full Text:
    approximate inference for Bayesian networks. We summarize the ideas underlying each algorithm and the