Description:
lsqfitgp 0.20.2
lsqfitgp
Python module to do inference with Gaussian processes. Features:
Based on JAX.
Interoperates with gvar and
lsqfit to facilitate inexpert users.
Recursively structured covariates.
Apply arbitrary linear transformations to the processes, finite and
infinite.
Small PPL based on Gaussian
copulas
to specify the hyperparameters prior.
Rich
collection
of covariance functions.
Good GP versions of
BART
(Bayes Additive Regression Trees) and
BCF
(Bayesian Causal Forests).
See this report for the
theory behind lsqfitgp.
Installation
Python >= 3.8 required. Then:
$ pip install lsqfitgp
Documentation
The complete manual is available online at
gattocrucco.github.io/lsqfitgp/docs.
All the code is documented with docstrings, so you can also use the Python help
system directly from the shell:
>>> import lsqfitgp as lgp
>>> help(lgp)
>>> help(lgp.something)
or, in an IPython shell/Jupyter notebook/Spyder IDE, use the question mark
shortcut:
In [1]: lgp?
In [2]: lgp.something?
Similar libraries
stheno
tinygp
GPJax
See also Comparison of Gaussian process Software
on Wikipedia.
License
This software is released under the GPL.
Amongst other things, it implies that, if you release an adaptation of this
software, or even a program just importing it as external
library, you
have to release its code as open source with a license at least as strong as
the GPL.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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