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Filtering by: Subject Bayesian statistical decision theory Remove constraint Subject: Bayesian statistical decision theory Non-Academic Affiliation Oregon State University. Graduate School Remove constraint Non-Academic Affiliation: Oregon State University. Graduate School Resource Type Masters Thesis Remove constraint Resource Type: Masters Thesis

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  • A Bayesian approach to marine spatial planning

    Creator:
    Erhardt, Morgan William
    Abstract:
    The marine ecosystem provides a broad spectrum of services for a wide array of user groups. Such broad use of marine resources often results in use conflicts between ecosystem elements and human interests. To help mitigate these conflicts, the Bayesian Analysis for Spatial Siting (BASS) tool was developed to incorporate...
    Resource Type:
    Masters Thesis
    Full Text:
    example of decision processes using the BASS system. 2.1.1 Bayesian Belief Networks in Ecosystem

  • 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

  • Monitoring and diagnosis of a multi-stage manufacturing process using Bayesian networks

    Creator:
    Wolbrecht, Eric T.
    Abstract:
    This thesis describes the application of Bayesian networks for monitoring and diagnosis of a multi-stage manufacturing process, specifically a high speed production part at Hewlett Packard. Bayesian network "part models" were designed to represent individual parts in-process. These were combined to form a "process model", which is a Bayesian network...
    Resource Type:
    Masters Thesis
    Full Text:
    process using Bayesian networks AN ABSTRACT OF THE THESIS OF Eric T. Wolbrecht for the degree of Master

  • 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

  • Parameter estimation of Gaussian hierarchical model using Gibbs sampling

    Creator:
    Mbuthia, Juliana
    Abstract:
    Gibbs sampling method is an important tool used in parameter estimation for many probabilistic models. Specifically, for many scenarios, it is difficult to generate high-dimensional data samples from its joint distribution. The Gibbs sampling provides a way to draw high-dimensional data via the conditional distributions which are typically easier to...
    Resource Type:
    Masters Thesis
    Full Text:
    probability, Bayesian analysis, graph theory and inference algorithms [10]. Will explain some of the details