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Filtering by: Creator Kapuscinski, Pearl B. Remove constraint Creator: Kapuscinski, Pearl B. Non-Academic Affiliation Oregon State University. Graduate School Remove constraint Non-Academic Affiliation: Oregon State University. Graduate School Subject Bayesian statistical decision theory Remove constraint Subject: Bayesian statistical decision theory

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  • Optimal inference with local expressions

    Creator:
    Borujerdi, Mohammad
    Abstract:
    Probabilistic inference using Bayesian networks is now a well-established approach for reasoning under uncertainty. Among many e ciency-driven tech- niques which have been developed, the Optimal Factoring Problem (OFP) is distinguished for presenting a combinatorial optimization point of view on the problem. The contribution of this thesis is to extend...
    Resource Type:
    Dissertation
    Full Text:
    dependence of D on A and B, following Pearl (Pearl, 1988): P (D = t) = 1� (1� cA(D))(1� cB(D)) P (D = f

  • Representations and algorithms for efficient inference in Bayesian networks

    Creator:
    Takikawa, Masami
    Abstract:
    Bayesian networks are used for building intelligent agents that act under uncertainty. They are a compact representation of agents' probabilistic knowledge. A Bayesian network can be viewed as representing a factorization of a full joint probability distribution into the multiplication of a set of conditional probability distributions. Independence of causal...
    Resource Type:
    Dissertation
    Full Text:
    conditional probabilities that must be specified. A Bayesian network [Pearl, 1988] is a directed acyclic

  • Dependent evidence in reasoning with uncertainty

    Creator:
    Ling, Xiaoning
    Abstract:
    The problem of handling dependent evidence is an important practical issue for applications of reasoning with uncertainty in artificial intelligence. The existing solutions to the problem are not satisfactory because of their ad hoc nature, complexities, or limitations. In this dissertation, we develop a general framework that can be used...
    Resource Type:
    Dissertation
    Full Text:
    which deal with uncertain information such as Pearl's belief networks [Pearl 1986] and Shafer's Markov

  • Spatial graphical models with discrete and continuous components

    Creator:
    Che, Xuan
    Abstract:
    Graphical models use Markov properties to establish associations among dependent variables. To estimate spatial correlation and other parameters in graphical models, the conditional independences and joint probability distribution of the graph need to be specified. We can rely on Gaussian multivariate models to derive the joint distribution when all the...
    Resource Type:
    Dissertation
    Full Text:
    CONCLUSION 147 APPENDIX A Notes on Hammersley-Clifford theorem 154 APPENDIX B Exponential family

  • 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:
    the ICPT tables of the 10 Burglary P(B) 0.001 Alarm Earthquake B P(A) T T 095 T F 0.94 F T

  • 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:
    ................................................. 100 Appendix B: Raw Data for Experiments with the Peaked Backward Simulation Algorithm