Global ETD Search
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Showing 1 to 5 of 5 for “"Gaussian Stochastic Process"”.
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Non-Gaussian Stochastic Process Priors for Learning
Non-Gaussian statistics naturally emerge as a fundamental concept in the study of real-world phenomena where standard Gaussian models often fall short in capturing the true variability and extreme behaviour. These characteristics are especially prevalent in fields such as finance, climate science, …
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Adapting Response Surface Methods for the Optimization of Black-Box Systems
… models are often built to describe a physical process that would otherwise be extremely difficult, too costly or sometimes impossible to analyze. Generally, these models require solutions to many partial differential equations. As a result, the computer codes may take a considerable amount of …
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Valid estimation and prediction inference in analysis of a computer model
… code as a random function, also known as a Gaussian stochastic process. We concern ourselves with smooth response surfaces and use the Gaussian covariance function that is ideal in cases when the response function is infinitely differentiable. In this thesis, we propose Fast Bayesian …
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Parametric Estimation of the Heston Model under the Indirect Observability Framework
Estimating parameters in a given stochastic model from a discrete dataset has wide applications in various scientific studies. However, it is also common that the available data are not generated from the stochastic model under investigation, but come from some other sources. For instance, realized …
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On Uncertainty Quantification for Systems of Computer Models
<p>Scientific inquiry about natural phenomena and processes are increasingly relying on the use of computer models as simulators of such processes. The challenge of using computer models for scientific investigation is that they are expensive in terms of computational cost and resources. However, …