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Showing 1 to 20 of 93 for “"Gaussian Process Regression."”.
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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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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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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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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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Scalable Approximate Inference and Model Selection in Gaussian Process Regression
Models with Gaussian process priors and Gaussian likelihoods are one of only a handful of Bayesian models where inference can be performed without the need for approximation. However, a frequent criticism of these models from practitioners of Bayesian machine learning is that they are challenging …
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Gaussian process regression approach to pricing multi-asset American options
… using machine learning. In particular, the Gaussian Process Regression Monte Carlo (GPR-MC) algorithm developed by Goudenege et al (2019). is explored, and ` its performance, i.e., its accuracy and efficiency, is benchmarked against the Least Squares Regression Method (LSM) developed by …
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Reduced-space Gaussian process regression forecast for nonlinear dynamical systems
… 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 …
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Efficient Prediction of Quantum Chemical Properties with Multitask Gaussian Process Regression
… specifically focus on inference methods based on Gaussian process (GP) regression. One example of such an approach, the Delta method, uses GP regression to model the difference between two different observation data sets, in our case CCSD(T) and DFT. The multitask method, by contrast, models a …
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Applications of Gaussian Process Regression to the Pricing and Hedging of Exotic Derivatives
… The purpose of this research is to apply the Gaussian Process Regression (GPR) method to the pricing and hedging of exotic options under the Black-Scholes and Heston model. GPR is a supervised machine learning technique which makes use of a training set to train an algorithm so that it makes …
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DAVMAS-GP: Domain aware variance minimizing Gaussian process regression for complex monostatic RCS prediction
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01
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An automatic, multi-fidelity framework for optimizing the performance of super-cavitating hydrofoils using Gaussian process regression and Bayesian optimization
… 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 …
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A Search For New Low-Mass Diphoton Resonances At Atlas And An Investigation Into Using Gaussian Process Regression To Model Non-Resonant Two-Photon Standard Model Backgrounds
… of a new method to model this background using Gaussian Process Regression.
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Machine learning for pricing European basket options
… to deploy machine learning techniques such as Gaussian process regression to approximate the European basket option prices. For the underlying asset of European basket option, we assume it follows multivariate Black \&\ Scholes model, and we can derive the PDE for the option price. In order to …
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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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Enhanced Navigation Using Aerial Magnetic Field Mapping
… from a quadrotor is comparatively performed with Gaussian process regression, a multiplicative extended Kalman filter, and a particle filter to estimate the position and attitude of an uncrewed aircraft system (UAS) at any point in the motion capture testing environment. Motion capture truth data …
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Structural performance evaluation of concrete arch dams using ambient vibration monitoring and GNNS systems
… a machine learning-based algorithm known as Gaussian process regression models were developed to predict natural frequencies. In Gaussian process regression, the choice of a covariance function is very important in producing good results. The ability of the different covariance functions in …
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A targeted reverse mapping machine learning approach for non-dominated solutions in multi-objective optimization
… This study proposes a framework using Gaussian process regression and artificial neural networks to generate innovative solutions in the ROI. By employing diverse sampling techniques and integrating long term memory, the framework can produce more than twice as many solutions in the …
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Estimating the Performance of Optical Fibre Communication Systems
… the receiver end, following the digital signal processing phase. First, we reconsider the application of the multicanonical Monte Carlo method to estimate the very low bit error rate of low-density parity-check codes, all while employing a parallel belief-propagation decoder implementation. …
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