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

Surrogate Final BH properties

Differentiable Gaussian Process implementation for PyTorch

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

Sparse Spectrum Gaussian Process Regression

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

Modern C++ library handling gaussian processes

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

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

Multi Kernel Linear Mixed Models for Complex Phenotype Prediction

Modelling stellar activity signals with Gaussian process regression networks

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