exoplanet
exoplanet: Gradient-based probabilistic inference for exoplanet data & other astronomical time series - Published in JOSS (2021)
egobox, a Rust toolbox for efficient global optimization
egobox, a Rust toolbox for efficient global optimization - Published in JOSS (2022)
AutoEmulate
AutoEmulate: A Python package for semi-automated emulation - Published in JOSS (2025)
PyVBMC
PyVBMC: Efficient Bayesian inference in Python - Published in JOSS (2023)
approxposterior
approxposterior: Approximate Posterior Distributions in Python - Published in JOSS (2018)
starry_process
starry_process: Interpretable Gaussian processes for stellar light curves - Published in JOSS (2021)
kima
kima: Exoplanet detection in radial velocities - Published in JOSS (2018)
GPJax
GPJax: A Gaussian Process Framework in JAX - Published in JOSS (2022)
epinow2
Estimate Realtime Case Counts and Time-varying Epidemiological Parameters
prob-epi
Course materials of "Bayesian Modelling and Probabilistic Programming with Numpyro, and Deep Generative Surrogates for Epidemiology"
skbel
SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.
distnav
(RSS 2021) Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation
mastercurves
Python package for automatically superimposing data sets to create a master curve, using Gaussian process regression and maximum a posteriori estimation.
stk
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.
https://github.com/juliagaussianprocesses/abstractgps.jl
Abstract types and methods for Gaussian Processes.
https://github.com/ywx649999311/eztao
A Python Toolkit for AGN Time Series Analysis using CARMA models
https://github.com/juliagaussianprocesses/temporalgps.jl
Fast inference for Gaussian processes in problems involving time. Partly built on results from https://proceedings.mlr.press/v161/tebbutt21a.html
spectralcov-gp-mtgp
Gaussian Processes for Vegetation Parameter Estimation from Hyperspectral Data with Limited Ground Truth
https://github.com/jbrea/bayesianoptimization.jl
Bayesian optimization for Julia
varycoef
The R package varycoef implements Gaussian processes spatially varying coefficient models.
https://github.com/alan-turing-institute/mogp-emulator
Package for fitting Gaussian Process Emulators to multiple output computer simulation results.
lgpr
R-package for interpretable nonparametric modeling of longitudinal data using additive Gaussian processes. Contains functionality for inferring covariate effects and assessing covariate relevances. Various models can be specified using a convenient formula syntax.
https://github.com/SepKfr/Coarse-and-Fine-Grained-Forecasting-Via-GP-Blurring-Effect
Forecast-blur-denoise forecasting model with PyTorch
https://github.com/biaslab/ccta2024-bidconvection
Experiments for CCTA 2024 submission on fast Bayesian gray-box identification of convection in heat transfer dynamics.
https://github.com/cheind/rgbd-correction
Code and data accompanying our work on spatio-thermal depth correction of RGB-D sensors based on Gaussian Process Regression in real-time.
Limbo
Limbo: A Flexible High-performance Library for Gaussian Processes modeling and Data-Efficient Optimization - Published in JOSS (2018)
augmentedgaussianprocesses.jl
Gaussian Process package based on data augmentation, sparsity and natural gradients
https://github.com/charlesll/gpvisc
Gaussian Process - Artificial Neural Network modelling of magma viscosity.
https://github.com/mlefkir/pioran.jl
Power spectrum inference of irregularly sampled time series using Gaussian Processes in Julia
https://github.com/aaltoml/t-svgp
Codes for 'Dual Parameterization of Sparse Variational Gaussian Processes' (NeurIPS 2021)
https://github.com/alan-turing-institute/gaussianprocessestutorial
An introduction to Gaussian processes with Julia
med
Autonomously driving equation discovery, from the micro to the macro, from laptops to supercomputers.
https://github.com/bandframework/surmise
A python package for surrogate models that interface with calibration and other tools
https://github.com/aaltoml/nonstationary-audio-gp
End-to-End Probabilistic Inference for Nonstationary Audio Analysis
https://github.com/aaltoml/kalman-jax
Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX
https://github.com/aaltoml/sfr
PyTorch implementation of Sparse Function-space Representation of Neural Networks
https://github.com/cyberagentailab/preferentialbo
(ICML2023) Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes
https://github.com/aaltoml/improved-hyperparameter-learning
Codes for 'Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models' (ICML 2023)
https://github.com/aaltoml/spatio-temporal-gps
Code for NeurIPS 2021 paper 'Spatio-Temporal Variational Gaussian Processes'
https://github.com/aaltoml/boundary-gp
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
https://github.com/acerbilab/vbmc
Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference in MATLAB
https://github.com/bmorris3/gadfly
Simulate stellar oscillations and granulation with Gaussian processes 🌟
cocons
R package for nonstationary spatial modeling with covariate-based covariance functions
https://github.com/aaltoml/sfr-experiments
Code accompanying ICLR 2024 paper "Function-space Parameterization of Neural Networks for Sequential Learning"
https://github.com/aaltoml/hilbert-gp
Codes for Hilbert space reduced-rank GP regression
pye-plus
Multi-Criteria Decision Making (MCDM) Framework for Building Energy Systems with Expedited Computation using Machine Learning (ML) Techniques
linpde-gp
Code for the Paper "Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers"
https://github.com/aaltoml/gp-mvs
Multi-View Stereo by Temporal Nonparametric Fusion
https://github.com/ashrithsagar/multireflfd-tpgp
Learning Multi-Reference Frame Skills from Demonstration with Task-Parameterized Gaussian Processes (TPGP)
fickleheart-method-tutorials
Code for the tutorials in the Fickle Heart model calibration with discrepancy 2020 paper.