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  • 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