Gaussian process convolutions for Bayesian spatial classification

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Show simple item record Best, John K. 2017-12-13T01:36:36Z 2017-12-13T01:36:36Z 2016-05
dc.description Master's Project (M.S.) University of Alaska Fairbanks, 2016 en_US
dc.description.abstract We compare three models for their ability to perform binary spatial classification. A geospatial data set consisting of observations that are either permafrost or not is used for this comparison. All three use an underlying Gaussian process. The first model considers this process to represent the log-odds of a positive classification (i.e. as permafrost). The second model uses a cutoff. Any locations where the process is positive are classified positively, while those that are negative are classified negatively. A probability of misclassification then gives the likelihood. The third model depends on two separate processes. The first represents a positive classification, while the second a negative classification. Of these two, the process with greater value at a location provides the classification. A probability of misclassification is also used to formulate the likelihood for this model. In all three cases, realizations of the underlying Gaussian processes were generated using a process convolution. A grid of knots (whose values were sampled using Markov Chain Monte Carlo) were convolved using an anisotropic Gaussian kernel. All three models provided adequate classifications, but the single and two-process models showed much tighter bounds on the border between the two states. en_US
dc.language.iso en_US en_US
dc.subject Gaussian processes en_US
dc.subject Spatial analysis (Statistics) en_US
dc.title Gaussian process convolutions for Bayesian spatial classification en_US
dc.type Other en_US ms
dc.identifier.department Department of Mathematics and Statistics
dc.contributor.chair Short, Margaret
dc.contributor.committee Goddard, Scott
dc.contributor.committee Barry, Ron
dc.contributor.committee McIntyre, Julie

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