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 ...
EurekAlert!

Flowchart of the land subsidence modeling and simulation combining peridynamics and deep learning (IMAGE)

Based on using peridynamics to describe the physical processes of regional land subsidence, deep learning methods, including neural networks and Gaussian Process Regression, are employed to construct ...
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 ...
JSTOR Daily

Bayesian log-Gaussian Cox process regression: applications to meta-analysis of neuroimaging working memory studies

Pantelis Samartsidis, Claudia R. Eickhoff, Simon B. Eickhoff, Tor D. Wager, Lisa Feldman Barrett, Shir Atzil, Timothy D. Johnson, Thomas E. Nichols Journal of the ...
JSTOR Daily

On Residual Empirical Processes of Stochastic Regression Models with Applications to Time Series

Motivated by Gaussian tests for a time series, we are led to investigate the asymptotic behavior of the residual empirical processes of stochastic regression models. These models cover the fixed ...