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tinygp
User Guide
Why tinygp?
Installation Guide
Troubleshooting
Comparison With george
API
Tutorials
Tutorial: Getting Started
Tutorial: Modeling Frameworks
Tutorial: Multivariate Data
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
C
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D
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E
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F
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G
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K
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L
|
M
|
N
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P
|
R
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S
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T
C
Cholesky (class in tinygp.transforms)
condition() (tinygp.GaussianProcess method)
condition_and_predict() (tinygp.GaussianProcess method)
Constant (class in tinygp.kernels)
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)
F
from_parameters() (tinygp.transforms.Cholesky class method)
G
GaussianProcess (class in tinygp)
K
Kernel (class in tinygp.kernels)
L
Linear (class in tinygp.transforms)
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)