Browsing by Subject "Gaussian process"
Now showing items 1-9 of 9
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(2016-04-08)Spectrometers are the cornerstone of analytical chemistry. Recent advances in microoptics manufacturing provide lightweight and portable alternatives to traditional spectrometers. In this dissertation, we developed a ...
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(2021-06-02)Gaussian processes are a powerful and flexible class of nonparametric models that use covariance functions, or kernels, to describe correlations across data. In addition to expressing realistic assumptions, correlation ...
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(2010-01-14)This dissertation presents a Bayesian hierarchical model to combine two-resolution metrology data for inspecting the geometric quality of manufactured parts. The high- resolution data points are scarce, and thus scatter ...
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(2017-07-27)State estimation is a key function in the supervisory control and planning of an electric power grid. Typically, the independent system operator (ISO) runs least-squares based static state estimation once every few minutes. ...
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(2022-08-29)Surface polishing is a multi-stage process. Different abrasive tools and process parameters are employed at different stages. Tool change and endpoint decisions currently rely on practitioners’ subjective inspections and ...
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(2022-07-10)This dissertation contains four projects involving applications of truncated multivariate normal sampling and multivariate normal probability estimation for linearly constrained domains. These two problems have a large ...
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(2015-08-10)Classical statistical models encounter the computational bottleneck for large spatial/spatio-temporal datasets. This dissertation contains three articles describing computationally efficient approximation methods for ...
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Trends in Analogic Design: Highly Reconfigurable Filters, Performance Optimization and IP Protection (2021-03-25)The continuous technology scaling and rapid growth of applications involving a vast and diverse network of interconnected devices increase analog integrated circuit (IC) design complexity. This work addresses three main ...
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(2018-03-09)Bayesian statistical methods are known for their flexibility in modeling. This flexibility is possible because parameters can often be estimated via Markov chain Monte Carlo methods. In large datasets or models with many ...