Probabilistic Programming with Gaussian processes in Julia

Julia package for kernel functions for machine learning

Abstract types and methods for Gaussian Processes.

Gaussian Process package based on data augmentation, sparsity and natural gradients

Fast inference for Gaussian processes in problems involving time. Partly built on results from https://proceedings.mlr.press/v161/tebbutt21a.html

Bayesian optimization for Julia

Provides likelihood functions for Gaussian Processes.

High Quality Geophysical Analysis provides a general purpose Bayesian and deterministic inversion framework for various geophysical methods and spatially distributed / timeseries data

Lazy, structured, and efficient operations with kernel matrices.

Gaussian process regression

Code for Deep Structured Mixtures of Gaussian Processes (DSMGPs)

Built-in solvers for the GeoStats.jl framework

Julia package for KF and EKF parameter estimation using Automatic Differentiation

Bayesian optimization is a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, wh…

Network inference for gene expression using gradient matching. Final research project as part of the MSc in Bioinformatics and Theorectical Systems Biology at Imperial College London 2016/2017.

BCGP_ECI(Bayesian Constrained Gaussian Proocess model for Extrapolations in CI methods)

A package for fitting (curve restricted) smoothing splines of degrees 2p-1 using the Gaussian process view with a rank structured kernel matrix.

Bayesian Optimisation for Parameter Tuning of the XOR Neural Network

Geostatistical processes for the GeoStats.jl framework

Geostatistical simulation solvers for the GeoStats.jl framework