Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 326 for “"gaussian process"”.
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Sparse Gaussian Process Approximations and Applications
… we will investigate methods which use Gaussian processes to represent distributions over functions. Gaussian process models require approximations in order to be practically useful. This thesis focuses on understanding existing approximations and investigating new ones tailored to …
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The Generalised Gaussian Process Convolution Model
This thesis formulates the Generalised Gaussian Process Convolution Model (GGPCM), which is a generalisation of the Gaussian Process Convolution Model presented by Tobar et al. [2015b]. The GGPCM provides a theoretical framework for nonparametric kernel models of multidimensional signals defined on …
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Solar power forecasting using Gaussian process regression
… The main goal of this thesis is to explore how Gaussian process predicting frameworks can be developed and used to predict global horiz0ontal irra- diance. Data on Global horizontal irrandiance and some weather variables collected from various meterological stations were made available through …
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Validating Gaussian Process Models in Computer Experiments
… thesis we present a methodology for validating Gaussian process models: Gaussian process emulators and simulator discrepancy models. A Gaussian process emulator is a representation of our beliefs about a mathematical model implemented in a computer program known as a simulator. By ``simulator …
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Scalable Gaussian process inference using variational methods
Gaussian processes can be used as priors on functions. The need for a flexible, principled, probabilistic model of functional relations is common in practice. Consequently, such an approach is demonstrably useful in a large variety of applications. Two challenges of Gaussian process modelling are …
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Computer Experimental Design for Gaussian Process Surrogates
… However, some computer experiments for complex processes are still computationally demanding. A surrogate model or emulator, is often employed as a fast substitute for the simulator. Meanwhile, a common challenge in computer experiments and related fields is to efficiently explore the input …
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Deep Gaussian Process Surrogates for Computer Experiments
Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional composition, in which intermediate GP layers warp the original inputs, providing flexibility to model non-stationary dynamics. Recent applications in machine learning favor approximate, optimization-based inference for fast …
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Gaussian Process Regression for Option Pricing and Hedging
… to investigate the accuracy and efficiency of Gaussian process regression (GPR) compared to traditional quantitative pricing algorithms. The GPR algorithm is applied to pricing a down-and-out barrier call option. Notably, Crepey and Dixon ´ (2019) propose an alternative method for computing the …
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Prediction interval modeling using Gaussian process quantile regression
In this thesis a methodology to construct prediction intervals for a generic black-box point forecast model is presented. The prediction intervals are learned from the forecasts of the black-box model and the actual realizations of the forecasted variable by using quantile regression on the …
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Computationally efficient Gaussian Process changepoint detection and regression
… i.e. generated from multiple switching processes. Existing methods for GP regression over non-stationary data include clustering and change-point detection algorithms. However, these methods require significant computation, do not come with provable guarantees on correctness and speed, …
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Efficient computer experiment designs for Gaussian process surrogates
… that can result in a wealth of knowledge. Gaussian processes (GPs) are highly desirable models for computer experiments for their predictive accuracy and uncertainty quantification. This dissertation addresses GP modeling when data abounds as well as GP adaptive design when simulator …
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Gaussian Process Emulation: Theory and Application to Coupled Physics
… depend on both space and time. We develop Gaussian process (GP) emulators as fast surrogates of computationally expensive coupled computer models for uncertainty estimation. A GP can be thought of as an interpolator for a limited number of computer runs of a physics simulator. The …
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Wind turbine dynamics identification using gaussian process machine learning
… necessitate a regression method which is able to process data in batches, updating predictions as new data becomes available.;Gaussian process machine learning is chosen as the regression approach best suited for application in this problem. However, a review of existing batched Gaussian process …
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Gaussian Process Regression for a Single Underlying Autocallable Security
… the use of the machine learning technique Gaussian Process Regression (GPR) as a faster pricing alternative to Monte Carlo simula- tions. The speed of calculation is of interest since prices are linked to fast moving market variables. We focus on the pricing of a single underlying …
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Improved Gaussian process approximations for spatial and flow fields
… a function - such as a spatial or flow field - Gaussian processes (GPs) are suitable probabilistic models. These can incorporate expert knowledge (priors), and data can be used to learn the model parameters. The cost of learning is high, motivating approximations, most notably variational …
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Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models
Gaussian processes are powerful nonparametric distributions over continuous functions that have become a standard tool in modern probabilistic machine learning. However, the applicability of Gaussian processes in the large-data regime and in hierarchical probabilistic models is severely limited by …
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Bayesian Active Structure Learning for Gaussian Process Probabilistic Programs
… to acquire? Structure learning techniques for Gaussian process (GP) probabilistic programs provide a rich framework for inferring qualitative structure in data. In this thesis, we improve the data-efficiency of probabilistic GP structure learning by extending it to the active learning setting. …
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Implementation of gaussian process models for non-linear system identification
… is concerned with investigating the use of Gaussian Process (GP) models for the identification of nonlinear dynamic systems. The Gaussian Process model is a non-parametric approach to system identification where the model of the underlying system is to be identified through the application …
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Advances in Bayesian Factor Modeling and Scalable Gaussian Process Regression
… factors drive structured variation therein. Gaussian process (GP) models, on the other hand, describe the association between variables using a distance-based covariance kernel. This dissertation introduces two novel extensions of Bayesian factor models driven by applied problems, and then …
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