Дисертації з теми "Gaussian process regression model"
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Srinivasan, Balaji Vasan. "Gaussian process regression for model estimation." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8962.
Повний текст джерелаThesis 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.
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.
Повний текст джерелаYi, 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.
Повний текст джерелаNguyen, Huong. "Near-optimal designs for Gaussian Process regression models." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1533983585774383.
Повний текст джерелаErich, Roger Alan. "Regression Modeling of Time to Event Data Using the Ornstein-Uhlenbeck Process." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1342796812.
Повний текст джерелаTietze, Nils [Verfasser], Ulrich [Akademischer Betreuer] Konigorski, and Oliver [Akademischer Betreuer] Nelles. "Model-based Calibration of Engine Control Units Using Gaussian Process Regression / Nils Tietze. Betreuer: Ulrich Konigorski ; Oliver Nelles." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2015. http://d-nb.info/1111909903/34.
Повний текст джерелаBarrett, James Edward. "Gaussian process regression models for the analysis of survival data with competing risks, interval censoring and high dimensionality." Thesis, King's College London (University of London), 2015. http://kclpure.kcl.ac.uk/portal/en/theses/gaussian-process-regression-models-for-the-analysis-of-survival-data-with-competing-risks-interval-censoring-and-high-dimensionality(fe3440e1-9766-4fc3-9d23-fe4af89483b5).html.
Повний текст джерелаXu, Li. "Statistical Methods for Variability Management in High-Performance Computing." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104184.
Повний текст джерелаDoctor of Philosophy
This dissertation focuses on three projects that are all related to statistical methods in performance variability management in high-performance computing (HPC). HPC systems are computer systems that create high performance by aggregating a large number of computing units. The performance of HPC is measured by the throughput of a benchmark called the IOZone Filesystem Benchmark. The performance variability is the variation among throughputs when the system configuration is fixed. Variability management involves studying the relationship between performance variability and the system configuration. In Chapter 2, we use several existing prediction models to predict the standard deviation of throughputs given different system configurations and compare the accuracy of predictions. We also conduct HPC system optimization using the chosen prediction model as the objective function. In Chapter 3, we use the mixture model to determine the number of modes in the distribution of throughput under different system configurations. In addition, we develop a model to determine the number of additional runs for future benchmark experiments. In Chapter 4, we develop a statistical model that can predict the throughout distributions given the system configurations. We also compare the prediction of summary statistics of the throughput distributions with existing prediction models.
Edwards, Adam Michael. "Precision Aggregated Local Models." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/102125.
Повний текст джерелаDoctor of Philosophy
Occasionally, when describing the relationship between two variables, it may be helpful to use a so-called ``non-parametric" regression that is agnostic to the function that connects them. Gaussian Processes (GPs) are a popular method of non-parametric regression used for their relative flexibility and interpretability, but they have the unfortunate drawback of being computationally infeasible for large data sets. Past work into solving the scaling issues for GPs has focused on ``divide and conquer" style schemes that spread the data out across multiple smaller GP models. While these model make GP methods much more accessible to large data sets they do so either at the expense of local predictive accuracy of global surface continuity. Precision Aggregated Local Models (PALM) is a novel divide and conquer method for GP models that is scalable for large data while maintaining local accuracy and a smooth global model. I demonstrate that PALM can be built quickly, and performs well predictively compared to other state of the art methods. This document also provides a sequential algorithm for selecting the location of each local model, and variations on the basic PALM methodology.
Chu, Shuyu. "Change Detection and Analysis of Data with Heterogeneous Structures." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78613.
Повний текст джерелаPh. D.
De, lozzo Matthias. "Modèles de substitution spatio-temporels et multifidélité : Application à l'ingénierie thermique." Thesis, Toulouse, INSA, 2013. http://www.theses.fr/2013ISAT0027/document.
Повний текст джерелаThis PhD thesis deals with the construction of surrogate models in transient and steady states in the context of thermal simulation, with a few observations and many outputs.First, we design a robust construction of recurrent multilayer perceptron so as to approach a spatio-temporal dynamic. We use an average of neural networks resulting from a cross-validation procedure, whose associated data splitting allows to adjust the parameters of these models thanks to a test set without any information loss. Moreover, the construction of this perceptron can be distributed according to its outputs. This construction is applied to the modelling of the temporal evolution of the temperature at different points of an aeronautical equipment.Then, we proposed a mixture of Gaussian process models in a multifidelity framework where we have a high-fidelity observation model completed by many observation models with lower and no comparable fidelities. A particular attention is paid to the specifications of trends and adjustement coefficients present in these models. Different kriging and co-krigings models are put together according to a partition or a weighted aggregation based on a robustness measure associated to the most reliable design points. This approach is used in order to model the temperature at different points of the equipment in steady state.Finally, we propose a penalized criterion for the problem of heteroscedastic regression. This tool is build in the case of projection estimators and applied with the Haar wavelet. We also give some numerical results for different noise specifications and possible dependencies in the observations
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.
Повний текст джерелаGrande, Robert Conlin. "Computationally efficient Gaussian Process changepoint detection and regression." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90670.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 150-160).
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 correctness and speed, and most algorithms only work in batch settings. This thesis presents an efficient online GP framework, GP-NBC, that leverages the generalized likelihood ratio test to detect changepoints and learn multiple Gaussian Process models from streaming data. Furthermore, GP-NBC can quickly recognize and reuse previously seen models. The algorithm is shown to be theoretically sample efficient in terms of limiting mistaken predictions. Our empirical results on two real-world datasets and one synthetic dataset show GP-NBC outperforms state of the art methods for nonstationary regression in terms of regression error and computational efficiency. The second part of the thesis introduces a Reinforcement Learning (RL) algorithm, UCRL-GP-CPD, for multi-task Reinforcement Learning when the reward function is nonstationary. First, a novel algorithm UCRL-GP is introduced for stationary reward functions. Then, UCRL-GP is combined with GP-NBC to create UCRL-GP-CPD, which is an algorithm for nonstationary reward functions. Unlike previous work in the literature, UCRL-GP-CPD does not make distributional assumptions about task generation, does not assume changepoint times are known, and does not assume that all tasks have been experienced a priori in a training phase. It is proven that UCRL-GP-CPD is sample efficient in the stationary case, will detect changepoints in the environment with high probability, and is theoretically guaranteed to prevent negative transfer. UCRL-GP-CPD is demonstrated empirically on a variety of simulated and real domains.
by Robert Conlin Grande.
S.M.
Aguilar, Fargas Joan. "Prediction interval modeling using Gaussian process quantile regression." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/100361.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 62-65).
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 distribution of which is not assumed. An independent meta-model that runs in parallel to the original point forecast model is responsible for learning and generating the prediction intervals, thus requiring no modification to the original setup. This meta-model uses both the inputs and output of the black-box model and calculates a lower and an upper bound for each of its forecasts with the goal that a predefined percentage of future realizations are included in the interval formed by both bounds. Metrics for the performance of the meta-model are established, paying special attention to the conditional interval coverage with respect to both time and the inputs. A series of cases studies are performed to determine the capabilities of this approach and to compare it to standard practices.
by Joan Aguilar Fargas.
S.M. in Engineering and Management
Marque-Pucheu, Sophie. "Gaussian process regression of two nested computer codes." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC155/document.
Повний текст джерелаThree types of observations of the system exist: those of the chained code, those of the first code only and those of the second code only. The surrogate model has to be accurate on the most likely regions of the input domain of the nested code.In this work, the surrogate models are constructed using the Universal Kriging framework, with a Bayesian approach.First, the case when there is no information about the intermediary variable (the output of the first code) is addressed. An innovative parametrization of the mean function of the Gaussian process modeling the nested code is proposed. It is based on the coupling of two polynomials.Then, the case with intermediary observations is addressed. A stochastic predictor based on the coupling of the predictors associated with the two codes is proposed.Methods aiming at computing quickly the mean and the variance of this predictor are proposed. Finally, the methods obtained for the case of codes with scalar outputs are extended to the case of codes with high dimensional vectorial outputs.We propose an efficient dimension reduction method of the high dimensional vectorial input of the second code in order to facilitate the Gaussian process regression of this code. All the proposed methods are applied to numerical examples
Kamrath, Matthew. "Extending standard outdoor noise propagation models to complex geometries." Thesis, Le Mans, 2017. http://www.theses.fr/2017LEMA1038/document.
Повний текст джерелаNoise engineering methods (e.g. ISO 9613-2 or CNOSSOS-EU) efficiently approximate sound levels from roads, railways, and industrial sources in cities. However, engineering methods are limited to only simple box-shaped geometries. This dissertation develops and validates a hybrid method to extend the engineering methods to more complicated geometries by introducing an extra attenuation term that represents the influence of a real object compared to a simplified object.Calculating the extra attenuation term requires reference calculations to quantify the difference between the complex and simplified objects. Since performing a reference computation for each path is too computationally expensive, the extra attenuation term is linearly interpolated from a data table containing the corrections for many source and receiver positions and frequencies. The 2.5D boundary element method produces the levels for the real complex geometry and a simplified geometry, and subtracting these levels yields the corrections in the table.This dissertation validates this hybrid method for a T-barrier with hard ground, soft ground, and buildings. All three cases demonstrate that the hybrid method is more accurate than standard engineering methods for complex cases
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.
Повний текст джерелаUrry, 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.
Повний текст джерелаShah, 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.
Повний текст джерелаChen, Zexun. "Gaussian process regression methods and extensions for stock market prediction." Thesis, University of Leicester, 2017. http://hdl.handle.net/2381/40502.
Повний текст джерелаWan, Zhong Yi Ph D. Massachusetts Institute of Technology. "Reduced-space Gaussian process regression forecast for nonlinear dynamical systems." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104565.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 93-97).
In this thesis work, we formulate a reduced-order data-driven strategy for the efficient probabilistic forecast of complex high-dimensional dynamical systems for which data-streams are available. The first step of this method consists of the reconstruction of the vector field in a reduced-order subspace of interest using Gaussian Process Regression (GPR). GPR simultaneously allows for the reconstruction of the vector field, as well as the estimation of the local uncertainty. The latter is due to i) the local interpolation error and ii) due to the truncation of the high-dimensional phase space and it analytically quantified in terms of the GPR hyperparameters. The second step involves the formulation of stochastic models that explicitly take into account the reconstructed dynamics and their uncertainty. For regions of the attractor where the training data points are not sufficiently dense for GPR to be effective an adaptive blended scheme is formulated that guarantees correct statistical steady state properties. We examine the effectiveness of the proposed method to complex systems including the Lorenz 63, Lorenz 96, the Kuramoto-Sivashinsky, as well as a prototype climate model. We also study the performance of the proposed approach as the intrinsic dimensionality of the system attractor increases in highly turbulent regimes.
by Zhong Yi Wan.
S.M.
Kortesalmi, Linus. "Gaussian Process Regression-based GPS Variance Estimation and Trajectory Forecasting." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153126.
Повний текст джерелаSzlachta, Wojciech Jerzy. "First principles interatomic potential for tungsten based on Gaussian process regression." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648779.
Повний текст джерелаWågberg, 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.
Повний текст джерелаHoolohan, Victoria Ruth. "The use of Gaussian process regression for wind forecasting in the UK." Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/21544/.
Повний текст джерелаAlvarez, Mauricio A. "Convolved Gaussian process priors for multivariate regression with applications to dynamical systems." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/convolved-gaussian-process-priors-for-multivariate-regression-with-applications-to-dynamical-systems(0fe42df3-6dce-48ec-a74d-a6ecaf249d74).html.
Повний текст джерелаSeidu, 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.
Повний текст джерелаAdamou, Maria. "Bayesian optimal designs for the Gaussian Process Model." Thesis, University of Southampton, 2014. https://eprints.soton.ac.uk/373881/.
Повний текст джерелаKapat, Prasenjit. "Role of Majorization in Learning the Kernel within a Gaussian Process Regression Framework." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1316521301.
Повний текст джерелаWikland, Love. "Early-Stage Prediction of Lithium-Ion Battery Cycle Life Using Gaussian Process Regression." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273619.
Повний текст джерелаDatadriven prediktion av batterihälsa har fått ökad uppmärksamhet under de senaste åren, både inom akademin och industrin. Precisa prediktioner i tidigt stadium av batteriprestanda skulle kunna skapa nya möjligheter för produktion och användning. Genom att använda data från endast de första 100 cyklerna, i en datamängd med 124 celler där livslängden sträcker sig mellan 150 och 2300 cykler, kombinerar denna uppsats parametriska linjära modeller med ickeparametrisk Gaussisk processregression för att uppnå livstidsprediktioner med en genomsnittlig noggrannhet om 8.8% fel. Studien utgör ett relevant bidrag till den aktuella forskningen eftersom den använda kombinationen av metoder inte tidigare utnyttjats för regression av batterilivslängd med ett högdimensionellt variabelrum. Studien och de erhållna resultaten visar att regression med hjälp av Gaussiska processer kan bidra i framtida datadrivna implementeringar av prediktion för batterihälsa.
Fry, James Thomas. "Hierarchical Gaussian Processes for Spatially Dependent Model Selection." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/84161.
Повний текст джерелаPh. D.
Yang, Xiaoke. "Fault-tolerant predictive control : a Gaussian process model based approach." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708784.
Повний текст джерелаSu, Weiji. "Flexible Joint Hierarchical Gaussian Process Model for Longitudinal and Recurrent Event Data." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595850414934069.
Повний текст джерелаRezvani, Arany Roushan. "Gaussian Process Model Predictive Control for Autonomous Driving in Safety-Critical Scenarios." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-161430.
Повний текст джерелаParker, Benjamin W. (Benjamin Wade). "An automatic, multi-fidelity framework for optimizing the performance of super-cavitating hydrofoils using Gaussian process regression and Bayesian optimization." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118719.
Повний текст джерелаThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 99-100).
Computer automated design of complex physical systems is often limited by the computational resources required for the high precision solvers. Determining an optimum design necessitates high accuracy simulations due to the multi-dimensional design space and the interconnectedness of the constraint and objective quantities. This paper will present an automated framework for iterating through a design loop that includes both physics-based computer simulations and surrogate model training using machine learning techniques. To alleviate the computation burden and efficiently explore the design space, a surrogate model for each quantity of interest that cannot be found deterministically will be utilized. Further reduction of the computational cost is accomplished by utilizing both low- and high-fidelity data to build the response surfaces. These response surface models will be trained using multi-fidelity Gaussian process regression. The models will be iteratively improved using Bayesian optimization and additional high-fidelity simulations that are automatically initiated within the design loop. In addition, Bayesian optimization will be used to automatically determine the optimum kernel for the Gaussian regression model. The feasibility of this framework is demonstrated by designing a 2D super-cavitating hydrofoil and comparing the optimum shape found with a known benchmark design. This automated multi-fidelity Bayesian optimization framework can aid in taking the human out of the design loop, thus freeing manpower resources and removing potential human bias.
by Benjamin W. Parker.
Nav. E.
S.M.
Tavares, Ivo Alberto Valente. "Uncertainty quantification with a Gaussian Process Prior : an example from macroeconomics." Doctoral thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/21444.
Повний текст джерелаThis thesis may be broadly divided into 4 parts. In the first part, we do a literature review of the state of the art in misspecification in Macroeconomics, and what so far has been the contribution of a relatively new area of research called Uncertainty Quantification to the Macroeconomics subject. These reviews are essential to contextualize the contribution of this thesis in the furthering of research dedicated to correcting non-linear misspecifications, and to account for several other sources of uncertainty, when modelling from an economic perspective. In the next three parts, we give an example, using the same simple DSGE model from macroeconomic theory, of how researchers may quantify uncertainty in a State-Space Model using a discrepancy term with a Gaussian Process prior. The second part of the thesis, we used a full Gaussian Process (GP) prior on the discrepancy term. Our experiments showed that despite the heavy computational constraints of our full GP method, we still managed to obtain a very interesting forecasting performance with such a restricted sample size, when compared with similar uncorrected DSGE models, or corrected DSGE models using state of the art methods for time series, such as imposing a VAR on the observation error of the state-space model. In the third part of our work, we improved on the computational performance of our previous method, using what has been referred in the literature as Hilbert Reduced Rank GP. This method has close links to Functional Analysis, and the Spectral Theorem for Normal Operators, and Partial Differential Equations. It indeed improved the computational processing time, albeit just slightly, and was accompanied with a similarly slight decrease in the forecasting performance. The fourth part of our work delved into how our method would account for model uncertainty just prior, and during, the great financial crisis of 2007-2009. Our technique allowed us to capture the crisis, albeit at a reduced applicability possibly due to computational constraints. This latter part also was used to deepen the understanding of our model uncertainty quantification technique with a GP. Identifiability issues were also studied. One of our overall conclusions was that more research is needed until this uncertainty quantification technique may be used in as part of the toolbox of central bankers and researchers for forecasting economic fluctuations, specially regarding the computational performance of either method.
info:eu-repo/semantics/publishedVersion
Rezaie, Reza. "Gaussian Conditionally Markov Sequences: Theory with Application." ScholarWorks@UNO, 2019. https://scholarworks.uno.edu/td/2679.
Повний текст джерелаHeller, Collin M. "A computational model of engineering decision making." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50272.
Повний текст джерелаNagy, Béla. "Valid estimation and prediction inference in analysis of a computer model." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1561.
Повний текст джерелаFadikar, Arindam. "Stochastic Computer Model Calibration and Uncertainty Quantification." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/91985.
Повний текст джерелаDoctor of Philosophy
Mathematical models are versatile and often provide accurate description of physical events. Scientific models are used to study such events in order to gain understanding of the true underlying system. These models are often complex in nature and requires advance algorithms to solve their governing equations. Outputs from these models depend on external information (also called model input) supplied by the user. Model inputs may or may not have a physical meaning, and can sometimes be only specific to the scientific model. More often than not, optimal values of these inputs are unknown and need to be estimated from few actual observations. This process is known as inverse problem, i.e. inferring the input from the output. The inverse problem becomes challenging when the mathematical model is stochastic in nature, i.e., multiple execution of the model result in different outcome. In this dissertation, three methodologies are proposed that talk about the calibration and prediction of a stochastic disease simulation model which simulates contagion of an infectious disease through human-human contact. The motivating examples are taken from the Ebola epidemic in West Africa in 2014 and seasonal flu in New York City in USA.
Tran, Giang Thanh. "Developing a multi-level Gaussian process emulator of an Atmospheric General Circulation Model for palaeoclimate modelling." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/412553/.
Повний текст джерелаCheng, Si. "Hierarchical Nearest Neighbor Co-kriging Gaussian Process For Large And Multi-Fidelity Spatial Dataset." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613750570927821.
Повний текст джерелаXie, Guangrui. "Robust and Data-Efficient Metamodel-Based Approaches for Online Analysis of Time-Dependent Systems." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/98806.
Повний текст джерелаPh.D.
Metamodeling has been regarded as a powerful analysis tool to learn the input-output relationship of an engineering system with a limited amount of experimental data available. As a popular metamodeling method, Gaussian process regression (GPR) has been widely applied to analyses of various engineering systems whose input-output relationships do not depend on time. However, GPR-based metamodeling for time-dependent systems (TDSs), whose input-output relationships depend on time, is especially challenging due to three reasons. First, standard GPR cannot properly address temporal effects for TDSs. Second, standard GPR is typically not computationally efficient enough for real-time implementations in TDSs. Lastly, research on how to adequately quantify the uncertainty associated with the performance of GPR-based metamodeling is sparse. To fill this knowledge gap, this dissertation aims to develop novel modeling, sampling, and statistical analysis techniques for enhancing standard GPR to meet the requirements of practical implementations for TDSs. Effective solutions are provided to address the challenges encountered in GPR-based analyses of two representative stochastic TDSs, i.e., load forecasting in a power system and trajectory prediction for unmanned aerial vehicles (UAVs). Furthermore, an in-depth investigation on quantifying the uncertainty associated with the performance of stochastic kriging (a variant of standard GPR) is conducted, which sets up a foundation for developing robust GPR-based metamodeling techniques for analyses of more complex TDSs.
Cardamone, Salvatore. "An interacting quantum atoms approach to constructing a conformationally dependent biomolecular force field by Gaussian process regression : potential energy surface sampling and validation." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/an-interacting-quantum-atoms-approach-to-constructing-a-conformationally-dependent-biomolecular-force-field-by-gaussian-process-regression-potential-energy-surface-sampling-and-validation(508ed450-9033-4bc9-8522-690d5a7909eb).html.
Повний текст джерелаSjödin, Hällstrand Andreas. "Bilinear Gaussian Radial Basis Function Networks for classification of repeated measurements." Thesis, Linköpings universitet, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170850.
Повний текст джерелаTong, Xiao Thomas. "Statistical Learning of Some Complex Systems: From Dynamic Systems to Market Microstructure." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10917.
Повний текст джерелаStatistics
Hernandez, Moreno Andres Felipe. "A metamodeling approach for approximation of multivariate, stochastic and dynamic simulations." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43690.
Повний текст джерелаZhou, Yifan. "Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/41696/1/Yifan_Zhou_Thesis.pdf.
Повний текст джерелаLu, Min. "A Study of the Calibration Regression Model with Censored Lifetime Medical Cost." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/math_theses/14.
Повний текст джерелаHan, Gang. "Modeling the output from computer experiments having quantitative and qualitative input variables and its applications." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1228326460.
Повний текст джерела