Grammar based Kernel Composition Design using Gaussian Processes for Non-Parametric Regression
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2020-10-30
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en
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This Thesis describes a method to automatically compose a kernel to use in a non-parametric
causal inference design. Choosing a kernel that provides a good fit for the model in Gaussian
Process regression can be challenging. Through summation and multiplication composite
kernels get created, by comparing and elaborating the composite kernels results in the best fitting
kernel. This method is an extension to the Bayesian non-parametric quasi-experimental
design.
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Faculteit der Sociale Wetenschappen