Learning-aided 3D mapping

Minimal Implementation of Bayesian Optimization in JAX

A minimal implementation of Gaussian process regression in PyTorch

Library for doing GPR (Gaussian Process Regression) in OCaml. Comes with a command line application.

constrained/unconstrained multi-objective bayesian optimization package.

The STK is a (not so) Small Toolbox for Kriging. Its primary focus is on the interpolation/regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior.

A step-by-step guide for surrogate optimization using Gaussian Process surrogate model

SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.

Sparse Spectrum Gaussian Process Regression

Surrogate Final BH properties

Differentiable Gaussian Process implementation for PyTorch

Modern C++ library handling gaussian processes

Code and data accompanying our work on spatio-thermal depth correction of RGB-D sensors based on Gaussian Process Regression in real-time.

Python module providing a framework to trace individual edges in an image using Gaussian process regression.

Bayesian Inference. Parallel implementations of DREAM, DE-MC and DRAM.

Gaussian Process Regression for training data with noisy inputs and/or outputs

Interpolate grain boundary properties in a 5 degree-of-freedom sense via a novel distance metric.

Personal reimplementation of some ML algorithms for learning purposes

Modelling stellar activity signals with Gaussian process regression networks

Multi Kernel Linear Mixed Models for Complex Phenotype Prediction