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Filtering by: Subject Bayesian statistical decision theory Remove constraint Subject: Bayesian statistical decision theory Degree Field Computer Science Remove constraint Degree Field: Computer Science

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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:
    Bayesian networks as a combinatorial optimization problem. Parallel to value sets algebra theory local

  • 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

  • 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

  • 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:
    for Theory of Evidence 19 3.2.2. Statistical Dependence Parameters for Odds-likelihood-ratios 21 3.3

  • Bayesian methods for knowledge transfer and policy search in reinforcement learning

    Creator:
    Wilson, Aaron
    Abstract:
    How can an agent generalize its knowledge to new circumstances? To learn effectively an agent acting in a sequential decision problem must make intelligent action selection choices based on its available knowledge. This dissertation focuses on Bayesian methods of representing learned knowledge and develops novel algorithms that exploit the represented...
    Resource Type:
    Dissertation
    Full Text:
    . Importantly, Bayesian theory provides an optimal means of updating the agent’s knowledge such that decisions

  • 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:
    Bayesian networks AN ABSTRACT OF THE THESIS OF Masami Takikawa for the degree of Doctor of Philosophy

  • 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