GPJax
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thomaspinder/GPJax
v0.13.6
594
73
GPJax
thomaspinder/GPJax
v0.13.6
594
73
🏡 Home
Getting started
Getting started
🛠️ Installation
🎨 Design principles
🤝 Contributing
🔪 Sharp bits
📎 JAX 101 [External]
💡 Background
💡 Background
Intro to GPs
Intro to Kernels
🎓 Tutorials
🎓 Tutorials
Regression
Classification
Poisson regression
Barycentres
Deep kernel learning
Graph kernels
Sparse GPs
Stochastic sparse GPs
Vector-Field GPs
Heteroscedastic Inference
Multi-Output GPs
Accelerated Multi-Output GPs
Orthogonal Additive GPs
🧪 Experimental
🧪 Experimental
Numpyro Integration
Spatial Linear Gaussian Process
📖 Guides for customisation
📖 Guides for customisation
Kernels
Likelihoods
Model Guide
UCI regression
Community
Community
👥 Code of conduct
📜 Governance
📨 Contact
📕 Reference
📕 Reference
GPJax
GPJax
Citation
Dataset
Distributions
Fit
GPs
Integrators
Kernels
Kernels
Additive
Additive
Decompose
Oak
Sobol
Transforms
Approximations
Approximations
RFF
Base
Computations
Computations
Base
Basis Functions
Constant Diagonal
Dense
Diagonal
Eigen
Multioutput
Multioutput
Base
Computation
Icm
Lcm
Non Euclidean
Non Euclidean
Graph
Utils
Nonstationary
Nonstationary
Arccosine
Linear
Polynomial
Stationary
Stationary
Base
Matérn12
Matérn32
Matérn52
Periodic
Powered Exponential
Rational Quadratic
RBF
Utils
White
Likelihoods
Linalg
Linalg
Operations
Operators
Utils
Mean Functions
Models
Models
Oilmm
Numpyro Extras
Objectives
Parameters
Scan
Typing
Variational Families
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