In this blog, we shall discuss on Gaussian Process Regression, the basic concepts, how it can be implemented with python from scratch and also using the GPy library. Then we shall demonstrate an ...

Gaussian process regression was designed for problems with strictly numeric predictor variables. However, GPR can be used with categorical predictor variables by using one-hot encoding. For example, ...

The updated version of GaPP (Gaussian Process in Python) with Python3. For the original version and the manual materials of GaPP please refere to Marina Seikel, Chris Clarkson, Mathew Smith, ...

GPyTorch is a PyTorch-based library designed for implementing Gaussian processes.It was introduced by Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger and Andrew Gordon Wilson – ...

Simulation of stationary random processes (time series) is an essential engineering tool for system prototyping, design, and optimization. To create a simulation, a randomly generated time series must ...

TITLE: Multi-Task Gaussian Process for Imputing Missing Daily Rainfall Data Using Nearby Stations: Case of Burkina Faso. AUTHORS: Souleymane Zio, Dazangwende Emmanuel Poan, Yoda Adaman, Kima Bénéwendé ...