GitHub

An Intuitive Tutorial to Gaussian Processes Regression

@misc{wang2020intuitive, title={An Intuitive Tutorial to Gaussian Processes Regression}, author={Jie Wang}, year={2020}, eprint={2009.10862}, archivePrefix={arXiv}, primaryClass={stat.ML} } The ...
Visual Studio Magazine

Gaussian Process Regression from Scratch Using C#

GPR works well with small datasets and generates a metric of confidence of a predicted result, but it's moderately complex and the results are not easily interpretable, says Dr. James McCaffrey of ...
Visual Studio Magazine

Gaussian Process Regression Using the scikit Library

A regression problem is one where the goal is to predict a single numeric value. For example, you might want to predict the price of a house based on its square footage, age, number of bedrooms and ...
IEEE

Physics-Constrained Gaussian Process Regression with Mag-Potential Kernel for Aeromagnetic Gradient Tensor Interpolation

Abstract: Aeromagnetic gradient tensor interpolation is a critical but challenging step in geophysical data processing, essential for transforming sparse, non-planar survey data into regular grids for ...
Risk

Gaussian process regression for derivative portfolio modeling and application to credit valuation adjustment computations

Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all trades between each counterparty under both market and credit risk. We present a ...
IEEE

Recursive Gaussian Process-Based Safety Assurance Exploration Control in Unknown Environments

Abstract: This letter presents a novel method for environmental exploration that takes safety into account in unknown areas by using recursive Gaussian process regression (RGPR). Safety in unknown ...