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Filtering by: Subject Bayesian statistical decision theory Remove constraint Subject: Bayesian statistical decision theory Decade 2010-2019 Remove constraint Decade: 2010-2019

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  • A Bayesian Treatise on the Replication Crisis in Science

    Creator:
    Gibson, True E.
    Abstract:
    Over the past decade, it has come to light that many published scientific findings cannot be reproduced. This has led to the replication crisis in science. Many researchers feel that they can no longer trust much of what they read in scientific journals, and the public is becoming ever more...
    Resource Type:
    Honors College Thesis
    Full Text:
    community ought to invoke a Bayesian framework of statistical inference which integrates both successful

  • 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

  • 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

  • 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

  • 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:
    incorporate some kind of spatial statistical models in their toolbox. Associations between variables and

  • Extensions to Gaussian copula models

    Creator:
    Fang, Yan
    Abstract:
    A copula is the representation of a multivariate distribution. Copulas are used to model multivariate data in many fields. Recent developments include copula models for spatial data and for discrete marginals. We will present a new methodological approach for modeling discrete spatial processes and for predicting the process at unobserved...
    Resource Type:
    Dissertation
    Full Text:
    . We employ Bayesian methodology for both estimation and prediction. Comparisons between the new

  • Improving soil water determination in spatially variable field using fiber optic technology and Bayesian decision theory

    Creator:
    Sayde, Chadi
    Abstract:
    Achieving and maintaining sustainability in irrigated agriculture production in the era of rapidly increasing stress on our natural resources require, among other essential actions, optimum control and management of the applied water. Thus, a significant upgrade of the currently available soil water monitoring technologies is needed. The primary goal of...
    Resource Type:
    Dissertation
    Full Text:
    Variable Field Using Fiber Optic Technology and Bayesian Decision Theory Abstract