Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods

Generating 3D CAD models with manifolds trained by Gaussian Process Latent Variable Models

multilinear gaussian process regression

AbstractGPs.jl is a package that defines a low-level API for working with Gaussian processes (GPs), and basic functionality for working with them in the simplest cases. As such it is aimed more at developers and researchers who are interested in using it as a building block than end-users of GPs.

Collection of Python code written during my time at Stephan Munch's Lab. Included is my implementation of a Gaussian Process called KenGP, which is a nonlinear, (mostly) nonparametric prediction tool. I also have implementations for Empirical Dynamical Modelling(like GP but much simpler), Convergent Cross Mapping(detects causal relationships bet…

deep-kernel-transfer no CUDA

This is a fork of the Evolving Gaussian process (EGP) modelling code, which is an extension of GP modelling to evolving systems. Such implementation facilitates sequential model adaptation to incoming data stream.

code for using GP for inferring the 2D lensing potential. Accompanying gaussian process repo is:

Learns an underlying process via Gaussian Process Regression

This repo holds the algorithm development for modeling TMS data with Gaussian processes and uses active learning to select optimum locations for the next stimulation.