These are some repos in which I have made relevant contributions. They are all used in my publications can be downloaded from my GitHub account
. Most of it is written in R, Matlab and Python. Please contact me if you have any question or comment.
is a Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
It is still in a pre-release phase. Together with, you can find the Emukit-playground
an interactive demo developed by Adam Hisrt useful to illustrate different concepts in emulation and uncertainty quantification.
is a library for Bayesian Optimization and experimental design. It is written in python.
is a small Python package to sample from determinantal point processes. It is based on the Matlab code of Alex Kulesza
and contains Python wrappers that make these methods usable in Python.
is a R-package estimating parameters of systems of Ordinary Differential equations. The method is based on a regularization approach in Reproducing kernel Hilbert Spaces.