Relevant bibliographies by topics / Gaussian process / Dissertations / Theses
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Dissertations / Theses on the topic 'Gaussian process'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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1

Gramacy, Robert B. "Bayesian treed Gaussian process models /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.

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2

Sofro, A'yunin. "Convolved Gaussian process regression models for multivariate non-Gaussian data." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/3723.

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Multivariate regression analysis has been developed rapidly in the last decade for dependent data. The most di cult part in multivariate cases is how to construct a crosscorrelation between response variables. We need to make sure that the covariance matrix is positive de nite which is not an easy task. Several approaches have been developed to overcome the issue. However, most of them have some limitations, such as it is hard to extend it to the case involving high dimensional variables or capture individual characteristics. It also should point out that the meaning of the cross-correlation s
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3

Serradilla, Javier. "Gaussian process models for process monitoring and control." Thesis, University of Newcastle Upon Tyne, 2012. http://hdl.handle.net/10443/1792.

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One problem of special interest both in industry and the engineering community is that of using the enormous amounts of data routinely generated and recorded in e client process monitoring and control strategies. In statistical terms this is related to identifying those variables which exhibit unwanted or unusual process variability so that remedial action can be taken. To this end, a common approach in the literature is to reduce the problem dimensionality by using latent variable models. Customarily, the latent variables are a function of all of the original variables and monitoring is carri
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4

Ou, Xiaoling. "Batch process modelling with Gaussian processes." Thesis, University of Newcastle Upon Tyne, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440591.

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5

van, der Wilk Mark. "Sparse Gaussian process approximations and applications." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/288347.

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Many tasks in machine learning require learning some kind of input-output relation (function), for example, recognising handwritten digits (from image to number) or learning the motion behaviour of a dynamical system like a pendulum (from positions and velocities now to future positions and velocities). We consider this problem using the Bayesian framework, where we use probability distributions to represent the state of uncertainty that a learning agent is in. In particular, we will investigate methods which use Gaussian processes to represent distributions over functions. Gaussian process mo
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6

Lopez, lopera Andres Felipe. "Gaussian Process Modelling under Inequality Constraints." Thesis, Lyon, 2019. https://tel.archives-ouvertes.fr/tel-02863891.

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Le conditionnement de processus gaussiens (PG) par des contraintes d’inégalité permet d’obtenir des modèles plus réalistes. Cette thèse s’intéresse au modèle de type PG proposé par maatouk (2015), obtenu par approximation finie, qui garantit que les contraintes sont satisfaites dans tout l’espace. Plusieurs contributions sont apportées. Premièrement, nous étudions l’emploi de méthodes de monte carlo par chaı̂nes de markov pour des lois multinormales tronquées. Elles fournissent un échantillonnage efficacpour des contraintes d’inégalité linéaires. Deuxièmement, nous explorons l’extension du mod
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Hanandeh, Ahmad Ali. "Nonstationary Nearest Neighbors Gaussian Process Models." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504781089107666.

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8

Srinivasan, Balaji Vasan. "Gaussian process regression for model estimation." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8962.

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Thesis (M.S.) -- University of Maryland, College Park, 2008.<br>Thesis research directed by: Dept. of Electrical and Computer Engineering E. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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9

Tran, Tien-Tam. "Constrained and Low Rank Gaussian Process on some Manifolds." Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2023. https://theses.hal.science/tel-04529284.

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La thèse est divisée en trois parties principales, nous résumerons les principales contributions de la thèse comme suit. Processus gaussiens à faible complexité : la régression par processus gaussien s'échelonne généralement en $O(n^3)$ en termes de calcul et en $O(n^2)$ en termes d'exigences de mémoire, où $n$ représente le nombre d'observations. Cette limitation devient inapplicable pour de nombreux problèmes lorsque $n$ est grand. Dans cette thèse, nous étudions l'expansion de Karhunen-Loève des processus gaussiens, qui présente plusieurs avantages par rapport aux techniques de compression
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10

Melis, Alessandro. "Gaussian process emulators for 1D vascular models." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/19175/.

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One-dimensional numerical models of the arterial vasculature are capable of simulating the physics of pulse wave transmission and reflection. These models are computationally efficient and represents and ideal choice with great translational opportunities in healthcare. However, the use of these models in a patient-specific scenario is hampered by the difficulty in measuring the model inputs (parameters, boundary conditions, and initial conditions) in the clinical setting. As a result, most of the model inputs are noisy or missing, and the inputs uncertainty is transmitted to the model outputs
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11

Saatçi, Yunus. "Scalable inference for structured Gaussian process models." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610016.

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12

Simek, Kyle. "Branching Gaussian Process Models for Computer Vision." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/612094.

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Bayesian methods provide a principled approach to some of the hardest problems in computer vision—low signal-to-noise ratios, ill-posed problems, and problems with missing data. This dissertation applies Bayesian modeling to infer multidimensional continuous manifolds (e.g., curves, surfaces) from image data using Gaussian process priors. Gaussian processes are ideal priors in this setting, providing a stochastic model over continuous functions while permitting efficient inference. We begin by introducing a formal mathematical representation of branch curvilinear structures called a curve tree
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13

Saul, Alan D. "Gaussian process based approaches for survival analysis." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/17946/.

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Traditional machine learning focuses on the situation where a fixed number of features are available for each data-point. For medical applications each individual patient will typically have a different set of clinical tests associated with them. This results in a varying number of observed per patient features. An important indicator of interest in medical domains is survival information. Survival data presents its own particular challenges such as censoring. The aim of this thesis is to explore how machine learning ideas can be transferred to the domain of clinical data analysis. We consider
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14

Matthews, Alexander Graeme de Garis. "Scalable Gaussian process inference using variational methods." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/278022.

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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 often encountered. These are dealing with the adverse scaling with the number of data points and the lack of closed form posteriors when the likelihood is non-Gaussian. In this thesis, we study variational inference as a framework for meeting these challenges. An introductory chapter motivates the use
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15

Wenzel, Florian. "Scalable Inference in Latent Gaussian Process Models." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/20926.

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Latente Gauß-Prozess-Modelle (latent Gaussian process models) werden von Wissenschaftlern benutzt, um verborgenen Muster in Daten zu er- kennen, Expertenwissen in probabilistische Modelle einfließen zu lassen und um Vorhersagen über die Zukunft zu treffen. Diese Modelle wurden erfolgreich in vielen Gebieten wie Robotik, Geologie, Genetik und Medizin angewendet. Gauß-Prozesse definieren Verteilungen über Funktionen und können als flexible Bausteine verwendet werden, um aussagekräftige probabilistische Modelle zu entwickeln. Dabei ist die größte Herausforderung, eine geeignete Inferenzmethode zu
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16

Zhang, Boya. "Computer Experimental Design for Gaussian Process Surrogates." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99886.

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With a rapid development of computing power, computer experiments have gained popularity in various scientific fields, like cosmology, ecology and engineering. 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 space using a small number of samples, i.e., the experimental design problem. This dissertation focuses on the design problem under Gaussian proces
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Hemakumara, Madu Prasad. "UAV Parameter Estimation with Gaussian Process Approximations." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9414.

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Unmanned Aerial Vehicles (UAVs) provide an alternative to manned aircraft for risk associated missions and applications where sizing constraints require miniaturized flying platforms. UAVs are currently utilised in an array of applications ranging from civilian research to military battlegrounds. A part of the development process for UAVs includes constructing a flight model. This model can be used for modern flight controller design and to develop high fidelity flight simulators. Furthermore, it also has a role in analysing stability, control and handling qualities of the platform. Developing
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18

Bastos, L. "Validating Gaussian process models in computer experiments." Thesis, University of Sheffield, 2010. http://etheses.whiterose.ac.uk/963/.

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In this 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 discrepancy'', we mean the difference between a simulator's output and the corresponding physical process. We present a set of diagnostics to validate and assess the adequacy of Gaussian process models. These diagnostics are based on comparisons between real observations and model predictions for
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19

Seidu, Mohammed Nazib. "Predicting Bankruptcy Risk: A Gaussian Process Classifciation Model." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119120.

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This thesis develops a Gaussian processes model for bankruptcy risk classification and prediction in a Bayesian framework. Gaussian processes and linear logistic models are discriminative methods used for classification and prediction purposes. The Gaussian processes model is a much more flexible model than the linear logistic model with smoothness encoded in the kernel with the potential to improve the modeling of the highly nonlinear relationships between accounting ratios and bankruptcy risk. We compare the linear logistic regression with the Gaussian process classification model in the con
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20

Yi, Gang. "Variable Selection with Penalized Gaussian Process Regression Models." Thesis, University of Newcastle upon Tyne, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.515061.

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21

Le, Gratiet Loic. "Multi-fidelity Gaussian process regression for computer experiments." Phd thesis, Université Paris-Diderot - Paris VII, 2013. http://tel.archives-ouvertes.fr/tel-00866770.

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This work is on Gaussian-process based approximation of a code which can be run at different levels of accuracy. The goal is to improve the predictions of a surrogate model of a complex computer code using fast approximations of it. A new formulation of a co-kriging based method has been proposed. In particular this formulation allows for fast implementation and for closed-form expressions for the predictive mean and variance for universal co-kriging in the multi-fidelity framework, which is a breakthrough as it really allows for the practical application of such a method in real cases. Furthe
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Grande, Robert Conlin. "Computationally efficient Gaussian Process changepoint detection and regression." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90670.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 150-160).<br>Most existing GP regression algorithms assume a single generative model, leading to poor performance when data are nonstationary, 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 cor
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Aguilar, Fargas Joan. "Prediction interval modeling using Gaussian process quantile regression." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/100361.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 62-65).<br>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 observed prediction error distribution, the distribut
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Olofsson, Simon. "Probabilistic Feature Learning Using Gaussian Process Auto-Encoders." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297752.

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The focus of this report is the problem of probabilistic dimensionality reduction and feature learning from high-dimensional data (images). Extracting features and being able to learn from high-dimensional sensory data is an important ability in a general-purpose intelligent system. Dimensionality reduction and feature learning have in the past primarily been done using (convolutional) neural networks or linear mappings, e.g. in principal component analysis. However, these methods do not yield any error bars in the features or predictions. In this report, theory and a model for how dimensional
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Chiu, Y. D. "Exploratory studies for Gaussian process structural equation models." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1437626/.

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Latent variable models (LVMs) are widely used in many scientific fields due to the ubiquitousness and feasibility of latent variables. Conventional LVMs, however, have limitations because they model relationships between covariates and latent variables or among latent variables with a parametric fashion. A more flexible model framework is therefore needed, especially without prior knowledge of sensible parametric forms. This thesis proposes a new non-parametric LVM for the need. We define a model structure with particular features, including a multi-layered structure constituting of non-parame
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26

Adamou, Maria. "Bayesian optimal designs for the Gaussian Process Model." Thesis, University of Southampton, 2014. https://eprints.soton.ac.uk/373881/.

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This thesis is concerned with methodology for finding Bayesian optimal designs for the Gaussian process model when the aim is precise prediction at unobserved points. The fundamental problem addressed is that the design selection criterion obtained from the Bayesian decision theoretic approach is often, in practice, computationally infeasible to apply. We propose an approximation to the objective function in the criterion and develop this approximation for spatial and spatio-temporal studies, and for computer experiments. We provide empirical evidence and theoretical insights to support the ap
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Nguyen, Huong. "Near-optimal designs for Gaussian Process regression models." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1533983585774383.

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28

Ottosson, Anton, and Viktor Karlstrand. "Gaussian Process Methods for Estimating Radio Channel Characteristics." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289449.

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Gaussian processes (GPs) as a Bayesian regressionmethod have been around for some time. Since proven advant-ageous for sparse and noisy data, we explore the potential ofGaussian process regression (GPR) as a tool for estimating radiochannel characteristics.Specifically, we consider the estimation of a time-varyingcontinuous transfer function from discrete samples. We introducethe basic theory of GPR, and employ both GPR and its deep-learning counterpart deep Gaussian process regression (DGPR)for estimation. We find that both perform well, even with fewsamples. Additionally, we relate the chann
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Marque-Pucheu, Sophie. "Gaussian process regression of two nested computer codes." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC155/document.

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Cette thèse traite de la métamodélisation (ou émulation) par processus gaussien de deux codes couplés. Le terme « deux codes couplés » désigne ici un système de deux codes chaînés : la sortie du premier code est une des entrées du second code. Les deux codes sont coûteux. Afin de réaliser une analyse de sensibilité de la sortie du code couplé, on cherche à construire un métamodèle de cette sortie à partir d'un faible nombre d'observations. Trois types d'observations du système existent : celles de la chaîne complète, celles du premier code uniquement, celles du second code uniquement.Le métamo
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Petit, Sébastien. "Improved Gaussian process modeling : Application to Bayesian optimization." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG063.

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Cette thèse s’inscrit dans la lignée de travaux portant sur la modélisation bayésienne de fonctions par processus gaussiens, pour des applications en conception industrielle s’appuyant sur des simulateurs numériques dont le temps de calcul peut atteindre jusqu’à plusieurs heures. Notre travail se concentre sur le problème de sélection et de validation de modèle et s’articule autour de deux axes. Le premier consiste à étudier empiriquement les pratiques courantes de modélisation par processus gaussien stationnaire. Plusieurs problèmes sur la sélection automatique de paramètre de processus gauss
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Wågberg, Johan, and Viklund Emanuel Walldén. "Continuous Occupancy Mapping Using Gaussian Processes." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81464.

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The topic of this thesis is occupancy mapping for mobile robots, with an emphasis on a novel method for continuous occupancy mapping using Gaussian processes. In the new method, spatial correlation is accounted for in a natural way, and an a priori discretization of the area to be mapped is not necessary as within most other common methods. The main contribution of this thesis is the construction of a Gaussian process library for C++, and the use of this library to implement the continuous occupancy mapping algorithm. The continuous occupancy mapping is evaluated using both simulated and real
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Contal, Emile. "Méthodes d’apprentissage statistique pour l’optimisation globale." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLN038/document.

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Cette thèse se consacre à une analyse rigoureuse des algorithmes d'optimisation globale équentielle. On se place dans un modèle de bandits stochastiques où un agent vise à déterminer l'entrée d'un système optimisant un critère. Cette fonction cible n'est pas connue et l'agent effectue séquentiellement des requêtes pour évaluer sa valeur aux entrées qu'il choisit. Cette fonction peut ne pas être convexe et contenir un grand nombre d'optima locaux. Nous abordons le cas difficile où les évaluations sont coûteuses, ce qui exige de concevoir une sélection rigoureuse des requêtes. Nous considérons d
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Fry, James Thomas. "Hierarchical Gaussian Processes for Spatially Dependent Model Selection." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/84161.

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In this dissertation, we develop a model selection and estimation methodology for nonstationary spatial fields. Large, spatially correlated data often cover a vast geographical area. However, local spatial regions may have different mean and covariance structures. Our methodology accomplishes three goals: (1) cluster locations into small regions with distinct, stationary models, (2) perform Bayesian model selection within each cluster, and (3) correlate the model selection and estimation in nearby clusters. We utilize the Conditional Autoregressive (CAR) model and Ising distribution to provide
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Jidling, Carl. "Strain Field Modelling using Gaussian Processes." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-315254.

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This report deals with reconstruction of strain fields within deformed materials. The method relies upon data generated from Bragg edge measurements, in which information is gained from neutron beams that are sent through the sample. The reconstruction has been made by modelling the strain field as a Gaussian process, assigned a covariance structure customized by incorporation of the so-called equilibrium constraints. By making use of an approximation scheme well suited for the problem, the complexity of the computations has been significantly reduced. The results from numerical simulations in
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Rosato, Andrea. "A Gaussian Process Learning Method for Nonlinear Optimal Control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22463/.

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This thesis is focused on discrete-time nonlinear optimal control techniques enhanced via a supervised learning approach based on Gaussian Process regression. Since optimal control strategies are strongly model-based, a perfect knowledge of the real system is required in order to obtain the best performances; however, it is not always possible to satisfy these requirements, since model uncertainties due to, e.g., unavailable information or hard to model dynamical effects may be present leading to a suboptimal solution for the real problem. The basic idea is to exploit measurement data to reduc
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Rafler, Mathias. "Gaussian loop- and Pólya processes : a point process approach." Phd thesis, Universität Potsdam, 2009. http://opus.kobv.de/ubp/volltexte/2009/3870/.

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This thesis considers on the one hand the construction of point processes via conditional intensities, motivated by the partial Integration of the Campbell measure of a point process. Under certain assumptions on the intensity the existence of such a point process is shown. A fundamental example turns out to be the Pólya sum process, whose conditional intensity is a generalisation of the Pólya urn dynamics. A Cox process representation for that point process is shown. A further process considered is a Poisson process of Gaussian loops, which represents a noninteracting particle system derived
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Rafler, Mathias. "Gaussian loop- and polya processes : a point process approach." Universität Potsdam, 2009. http://opus.kobv.de/ubp/volltexte/2011/5163/.

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Zufällige Punktprozesse beschreiben eine (zufällige) zeitliche Abfolge von Ereignissen oder eine (zufällige) räumliche Anordnung von Objekten. Deren wichtigster Vertreter ist der Poissonprozess. Der Poissonprozess zum Intensitätsmaß, das Lebesgue-Maß ordnet jedem Gebiet sein Volumen zu, erzeugt lokal, d.h in einem beschränkten Gebiet B, gerade eine mit dem Volumen von B poissonverteilte Anzahl von Punkten, die identisch und unabhängig voneinander in B plaziert werden; im Mittel ist diese Anzahl (B). Ersetzt man durch ein Vielfaches a, so wird diese Anzahl mit dem a-fachen Mittelwert erzeugt. P
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Gaigalas, Raimundas. "A Non-Gaussian Limit Process with Long-Range Dependence." Doctoral thesis, Uppsala : Matematiska institutionen, Univ. [distributör], 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3993.

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Zoghi, Masrour. "Regret bounds for Gaussian process bandits without observation noise." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42865.

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This thesis presents some statistical refinements of the bandits approach presented in [11] in the situation where there is no observation noise. We give an improved bound on the cumulative regret of the samples chosen by an algorithm that is related (though not identical) to the UCB algorithm of [11] in a complementary setting. Given a function f on a domain D ⊆ R^d , sampled from a Gaussian process with an anisotropic kernel that is four times differentiable at 0, and a lattice L ⊆ D, we show that if the points in L are chosen for sampling using our branch-and-bound algorithm, the regret asy
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Stone, Nicola. "Gaussian process emulators for uncertainty analysis in groundwater flow." Thesis, University of Nottingham, 2011. http://eprints.nottingham.ac.uk/11989/.

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In the field of underground radioactive waste disposal, complex computer models are used to describe the flow of groundwater through rocks. An important property in this context is transmissivity, the ability of the groundwater to pass through rocks, and the transmissivity field can be represented by a stochastic model. The stochastic model is included in complex computer models which determine the travel time for radionuclides released at one point to reach another. As well as the uncertainty due to the stochastic model, there may also be uncertainties in the inputs of these models. In order
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Rahman, Muhammad Arifur. "Gaussian process in computational biology : covariance functions for transcriptomics." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/19460/.

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In the field of machine learning, Gaussian process models are widely used families of stochastic process for modelling data observed over time, space or both. Gaussian processes models are nonparametric, meaning that the models are developed on an infinite-dimensional parameter space. The parameter space is then typically learnt as the set of all possible solutions for a given learning problem. Gaussian process distributions are distribution over functions. The covariance function determines the properties of functions samples drawn from the process. Once the decision to model with a Gaussian
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Davies, Alexander James. "Effective implementation of Gaussian process regression for machine learning." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708909.

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Urry, Matthew. "Learning curves for Gaussian process regression on random graphs." Thesis, King's College London (University of London), 2013. https://kclpure.kcl.ac.uk/portal/en/theses/learning-curves-for-gaussian-process-regression-on-random-graphs(c1f5f395-0426-436c-989c-d0ade913423e).html.

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Gaussian processes are a non-parametric method that can be used to learn both regression and classification rules from examples for arbitrary input spaces using the ’kernel trick’. They are well understood for inputs from Euclidean spaces, however, much less research has focused on other spaces. In this thesis I aim to at least partially resolve this. In particular I focus on the case where inputs are defined on the vertices of a graph and the task is to learn a function defined on the vertices from noisy examples, i.e. a regression problem. A challenging problem in the area of non-parametric
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Wu, Ruhao. "Gaussian process and functional data methods for mortality modelling." Thesis, University of Leicester, 2017. http://hdl.handle.net/2381/39143.

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Modelling the demographic mortality trends is of great importance due to its considerable impact on welfare policy, resource allocation and government planning. In this thesis, we propose to use various statistical methods, including Gaussian process (GP), principal curve, multilevel functional principal component analysis (MFPCA) for forecasting and clustering of human mortality data. This thesis is actually composed of three main topics regarding mortality modelling. In the first topic, we propose a new Gaussian process regression method and apply it to the modelling and forecasting of age-s
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Shah, Siddharth S. "Robust Heart Rate Variability Analysis using Gaussian Process Regression." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1293737259.

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Yan, Juan. "Advanced Gaussian process modelling for probabilistic wind power forecasting." Thesis, Queen's University Belfast, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709864.

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The demand for more sustainable social and economic development has resulted in a rapid growth in wind power generation largely due to the highly available of wind resource worldwide. Despite that various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional wind power methods, the stochastic nature of wind still remains the most challenging issue. A temporally local Gaussian process (TLGP) for time series forecasting is proposed to enhance the time varying adaptation and overcome the computation complexity problem. The iterative te
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Rafler, Mathias. "Gaussian loop- and Pólya processes a point process approach." Potsdam Univ.-Verl, 2009. http://d-nb.info/999884360/04.

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48

Snelson, Edward Lloyd. "Flexible and efficient Gaussian process models for machine learning." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1445855/.

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Gaussian process (GP) models are widely used to perform Bayesian nonlinear regression and classification tasks that are central to many machine learning problems. A GP is nonparametric, meaning that the complexity of the model grows as more data points are received. Another attractive feature is the behaviour of the error bars. They naturally grow in regions away from training data where we have high uncertainty about the interpolating function. In their standard form GPs have several limitations, which can be divided into two broad categories: computational difficulties for large data sets, a
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Bui, Thang Duc. "Efficient deterministic approximate Bayesian inference for Gaussian process models." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/273833.

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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 analytic and computational intractabilities. It is, therefore, important to develop practical approximate inference and learning algorithms that can address these challenges. To this end, this dissertation provides a comprehensive and unifying perspective of pseudo-point based deterministic approxima
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Ferguson, Bradley Thomas. "Adaptive Threat Detector Testing Using Bayesian Gaussian Process Models." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2728.

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Detection of biological and chemical threats is an important consideration in the modern national defense policy. Much of the testing and evaluation of threat detection technologies is performed without appropriate uncertainty quantification. This paper proposes an approach to analyzing the effect of threat concentration on the probability of detecting chemical and biological threats. The approach uses a probit semi-parametric formulation between threat concentration level and the probability of instrument detection. It also utilizes a bayesian adaptive design to determine at which threat conc
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