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 ...
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

Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data

In this article, we propose a generalized Gaussian process concurrent regression model for functional data, where the functional response variable has a binomial. Poisson, or other non-Gaussian ...
Boston College

Hierarchical Gaussian Process Regression for Meta-Learning of Molecular Geometry Optimization

Quantum chemistry uses quantum mechanics for the first-principle exploration of chemical systems. In principle, all chemical phenomena can be studied by solving the Schrödinger equation, the ...

New Breakthrough in Quantum Computing: Machine Learning Aids Efficient Noise Modeling

In the field of quantum computing, predicting and mitigating errors has always been a significant challenge for achieving efficient quantum computation. Recently, a research team from the Fraunhofer ...
Risk

Machine learning hedge strategy with deep Gaussian process regression

When applying machine learning to trading strategy, two inevitable practical issues are achieving interpretable results and securing robustness to market changes. To overcome these challenges, ...
Visual Studio Magazine

Gaussian Process Regression Using the scikit Library

This article assumes you have intermediate or better skill with a C-family programming language, but doesn't assume you know much about Gaussian process regression or the scikit library. The complete ...