Nested ratio estimation and inhomogeneous poisson point process sample caching for simulator efficient marginal posterior estimation.
Disclaimer: swyft is research software under heavy development and still in its alpha phase. There are many rough edges, and things might break. However, the core algorithms work, and we use swyft in production for research papers. If you encounter problems, please contact the authors or submit a bug report.
Neural nested marginal posterior estimation
Cursed by the dimensionality of your nuisance space? Wasted by Markov chains that reject your simulations? Exhausted from messing with simplistic models, because your inference algorithm cannot handle the truth? Try swyft for some pain relief.
For a quickstart guide, documentation, and more see readthedocs.
A simple example is avaliable on google colab.
After installing pytorch, please run the command:
pip install swyft
swyft exists in an ecosystem of posterior estimators. The project sbi is particularly relevant as it is a collection of likelihood-free / simulator-based methods.
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size swyft-0.1.2.tar.gz (986.0 kB)||File type Source||Python version None||Upload date||Hashes View|
|Filename, size swyft-0.1.2-py3-none-any.whl (24.1 kB)||File type Wheel||Python version py3||Upload date||Hashes View|