gaussian-process-optimisation

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.

Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods

Particle filter-based Gaussian process optimisation for parameter inference

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

Gaussian Process approximator in Bayesian inverse problems (MCMC)

Implementation of Ada-BKB a scalable Gaussian Process bandit optimization algorithm

This is a repository for implementing various Gaussian Processes (GPs) and also some notes regarding GPs from different lectures.

Automated tuning of SUPERSTRUCTURE’s Thomas-Fermi scaling parameters against NIST energy levels using a Gaussian-process surrogate optimiser. Works for any ion, makes level labels consistent with NIST, checks A-values and lifetimes against NIST, NORAD and measured values, and writes NORAD-format tables.