Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference in MATLAB

GPstuff - Gaussian process models for Bayesian analysis

This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).

Max-value Entropy Search for Efficient Bayesian Optimization

multivariate Gaussian process regression and multivariate Student-t process regression

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.

Code to implement efficient spatio-temporal Gaussian Process regression via iterative Kalman Filtering. KF is used to resolve the temporal part of the space-time process while, standard GP regression is used for the spatial part

Gaussian process regression + automatical model selection for logitudinal -omics data

Implementation for Non-stationary Spectral Kernels (NIPS 2017)

Codes for Hilbert space reduced-rank GP regression

Codebase for Cross-Spectral Factor Analysis (Gallagher et al., 2017)

End-to-End Probabilistic Inference for Nonstationary Audio Analysis

Particle filter-based Gaussian process optimisation for parameter inference

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

Disentangling Sources of Uncertainty for Active Exploration (Reinforcement Learning)

Implementation of the Expected Improvement and the Gaussian Process Upper Confidence Bound algorithm in MATLAB, as part of my Bachelor thesis @ ETHZ in 2014.

Learning-based robust sampled-data control for uncertain systems.

Project source code and data for multi-fidelity machine learning strategy for flame model identification

Code for testing a Model Reference Adaptive Control (MRAC) scheme to control the pitch of a plane under wing-rock dynamics, accounting for modeling errors by using Gaussian Process regression. Implementation based on [1].

MATLAB Implementation of the CGPRANK algorithm