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 163 for “"Gaussian processes"”.
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On Self-Similar Gaussian Processes
… a change-of-variable formula for a class of Gaussian processes with a covariance function satisfying minimal regularity and integrability conditions. The existence of the local time and a version of Tanaka's formula are derived. These results are applied to a general class of self-similar …
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Hierarchical Inference in Gaussian Processes
… hierarchical constructions in the context of Gaussian processes. Gaussian processes (GP) are flexible distribution over functions and exemplify the key attributes of probabilistic machine learning. They have the ability to encode a diverse range of statistical structures through the choice of …
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Two-parameter noncommutative Gaussian processes
… to a two-parameter deformation of the classical Gaussian statistics and (2) a two-parameter continuum of non-commutative probability spaces in which to realize these statistics. The framework that emerges has a remarkably rich combinatorial structure and bears upon a number of well-known …
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Automatic model construction with Gaussian processes
… and physical dynamics. These models, based on Gaussian processes, can capture many types of statistical structure, such as periodicity, changepoints, additivity, and symmetries. Such structure can be encoded through kernels, which have historically been hand-chosen by experts. We show how to …
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Gaussian processes in non-commutative probability theory
Contains fulltext : 19119.pdf (Publisher’s version ) (Open Access)
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Bayesian Time Series Learning with Gaussian Processes
… literature review on time series models based on Gaussian processes. Then, we centre our attention on the Gaussian Process State-Space Model (GP-SSM): a Bayesian nonparametric generalisation of discrete-time nonlinear state-space models. We present a novel formulation of the GP-SSM that offers new …
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Approximate Bayesian Modeling with Embedded Gaussian Processes
… inference methods. We propose the embedded Gaussian process framework to address these challenges. The embedded GP model captures the uncertainty of complex physical models and incorporates it in posterior inference where a joint distribution of all uncertain quantities, including the …
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Some Advances in Local Approximate Gaussian Processes
Nowadays, Gaussian Process (GP) has been recognized as an indispensable statistical tool in computer experiments. Due to its computational complexity and storage demand, its application in real-world problems, especially in "big data" settings, is quite limited. Among many strategies to tailor GP …
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Communication-Aware, Scalable Gaussian Processes for Decentralized Exploration
… decentralized and scalable algorithms for Gaussian process (GP) training and prediction in multi-agent systems. The first challenge is to compute a spatial field that represents underwater acoustic communication performance from a set of measurements. We compare kriging to cokriging with …
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Limit theorems for H-variation of Gaussian processes /
… H-variation and weighted H-variation of Gaussian processes with nonstationary increments are considered. The main problem of the dissertation is finding the limiting distribution of these statistics, the speed of convergence to the limit law and an proving invariance principle for the …
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Hierarchical Gaussian Processes for Spatially Dependent Model Selection
… modeling inter-cluster correlation with separate Gaussian processes. We apply our model selection methodology to a dataset involving the prediction of Brook trout presence in subwatersheds across Pennsylvania. We find that our methodology outperforms the stationary spatial model and that different …
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Linear Parameter Uncertainty Quantification using Surrogate Gaussian Processes
… uncertainty quantification using surrogate Gaussian processes. We take a previous sampling algorithm and provide a closed form expression of the resulting posterior distribution. We extend the method to weighted least squares and a Bayesian approach both with closed form expressions of the …
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Precipitation prediction over High Mountain Asia using Gaussian processes
… This work centres around one such method, Gaussian processes. First, Gaussian processes are applied to downscale ERA5 precipitation over ungauged areas. They are used to auto-regressively combine precipitation datasets with different fidelities, i.e. resolutions and accuracies. This …
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Optimization as estimation with Gaussian processes in bandit settings
… the assumption that the function is drawn from a Gaussian process (GP) with known priors. We propose an optimization strategy that directly uses a maximum a posteriori (MAP) estimate of the argmax of the function. This strategy offers both practical and theoretical advantages: no tradeoff …
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Gaussian Processes for Power System Monitoring, Optimization, and Planning
… using swift and probabilistic solutions. Gaussian process regression is a machine learning paradigm that provides closed-form predictions with quantified uncertainties. The key property of Gaussian processes is the natural ability to integrate the sensitivity of the labels with respect to …
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Scalable Gaussian Processes: Advances in Iterative Methods and Pathwise Conditioning
… leads to overconfidence and hallucinations. Gaussian processes are a powerful framework for uncertainty-aware function approximation and sequential decision-making. Unfortunately, their classical formulation does not scale gracefully to large amounts of data and modern hardware for …
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Bayesian optimization with Gaussian processes: Insights from hyperspectral trait search
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01
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Gaussian processes for state space models and change point detection
This thesis details several applications of Gaussian processes (GPs) for enhanced time series modeling. We first cover different approaches for using Gaussian processes in time series problems. These are extended to the state space approach to time series in two different problems. We also combine …
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Advances in Software and Spatio-Temporal Modelling with Gaussian Processes
This thesis concerns the use of Gaussian processes (GPs) as distributions over unknown functions in Machine Learning and probabilistic modeling. GPs have been found to have utility in a wide range of applications owing to their flexibility, interpretability, and tractability. I advance their use in …
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