Fit and compare complex models quickly. Laplace Approximation, Variational Bayes, Importance Sampling.
Project description
snowline
Fit and compare models very quickly. MCMC-free.
About
Posterior distributions and corner plots without MCMC? No dealing with convergence criteria?
Yes!
Tailored for low-dimensional (d<10) problems with a single mode, this package automatically finds the best fit and uses the local covariance matrix as a Laplace Approximation. Then Importance Sampling and Variational Bayes come in to improve from a single-gaussian approximation to more complex shapes. This allows sampling efficiently in some problems, and provides a estimate for the marginal likelihood.
This package is built on top the excellent (i)minuit and pypmc packages.
You can help by testing snowline and reporting issues. Code contributions are welcome. See the Contributing page.
Features
Pythonic. pip installable.
Easy to program for: Sanity checks with meaningful errors
Fast
MPI support
Usage
Read the full documentation at:
Licence
GPLv3 (see LICENCE file). If you require another license, please contact me.
Icon made by Vecteezy.
Other projects
See also:
UltraNest: https://johannesbuchner.github.io/UltraNest/
autoemcee: https://johannesbuchner.github.io/autoemcee/
Release Notes
0.4.0 (2020-03-07)
Improve robustness to poor Laplace approximations
0.3.0 (2020-05-07)
Numerical robustness
0.2.0 (2020-04-21)
Robustness improvements
Packaging and testing improvements
0.1.0 (2020-03-07)
First version
Release history Release notifications | RSS feed
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