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 (Bayesian Additive Regression Trees) and BCF (Bayesian Causal Forests).
See chapter 3 of my thesis for the theory behind lsqfitgp.
Python >= 3.10 required. Then:
$ pip install lsqfitgp
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?
See also Comparison of Gaussian process Software on Wikipedia.
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.