Julia package for kernel functions for machine learning
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Updated
May 8, 2024 - Julia
Julia package for kernel functions for machine learning
Probabilistic Programming with Gaussian processes in Julia
Abstract types and methods for Gaussian Processes.
Bayesian optimization for Julia
Gaussian Process package based on data augmentation, sparsity and natural gradients
Code for Deep Structured Mixtures of Gaussian Processes (DSMGPs)
Provides likelihood functions for Gaussian Processes.
Fast inference for Gaussian processes in problems involving time. Partly built on results from https://proceedings.mlr.press/v161/tebbutt21a.html
High Quality Geophysical Analysis provides a general purpose Bayesian and deterministic inversion framework for various geophysical methods and spatially distributed / timeseries data
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.
Lazy, structured, and efficient operations with kernel matrices.
Gaussian process regression
BOSS (Bayesian Optimization with Semiparametric Surrogate)
Spectral Gaussian simulation solver for the GeoStats.jl framework
Direct Gaussian simulation solver for the GeoStats.jl framework
Geostatistical simulation solvers for the GeoStats.jl framework
Julia package for KF and EKF parameter estimation using Automatic Differentiation
A package for fitting (curve restricted) smoothing splines of degrees 2p-1 using the Gaussian process view with a rank structured kernel matrix.
Gauss process integrated with forward automatic differentiation
BCGP_ECI(Bayesian Constrained Gaussian Proocess model for Extrapolations in CI methods)
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