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tinygp
User Guide
Why tinygp?
Installation Guide
API
Comparison With george
Tutorials
Tutorial: Getting Started
Tutorial: Modeling Frameworks
Tutorial: Kernel Transforms
Tutorial: Custom Kernels & Pytree Data
Tutorial: Non-Gaussian Likelihoods
Tutorial: Mixture of Kernels
Contributor Guide
Code of Conduct
GitHub Repository
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repository
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Index
A
|
C
|
D
|
E
|
G
|
K
|
M
|
N
|
P
|
R
|
S
|
T
|
Z
A
Affine (class in tinygp.transforms)
C
condition() (tinygp.GaussianProcess method)
condition_and_predict() (tinygp.GaussianProcess method)
Constant (class in tinygp.kernels)
constant_mean() (in module tinygp.means)
Cosine (class in tinygp.kernels)
Custom (class in tinygp.kernels)
D
DotProduct (class in tinygp.kernels)
E
evaluate() (tinygp.kernels.Kernel method)
Exp (class in tinygp.kernels)
ExpSineSquared (class in tinygp.kernels)
ExpSquared (class in tinygp.kernels)
G
GaussianProcess (class in tinygp)
K
Kernel (class in tinygp.kernels)
M
Matern32 (class in tinygp.kernels)
Matern52 (class in tinygp.kernels)
N
numpyro_dist() (tinygp.GaussianProcess method)
P
Polynomial (class in tinygp.kernels)
predict() (tinygp.GaussianProcess method)
R
RationalQuadratic (class in tinygp.kernels)
S
sample() (tinygp.GaussianProcess method)
Subspace (class in tinygp.transforms)
T
Transform (class in tinygp.transforms)
Z
zero_mean() (in module tinygp.means)