Written by Wei-Chia Chen, Ammar Tareen, and Justin B. Kinney.
SUFTware (Statistics Using Field Theory) provides fast and lightweight Python implementations of Bayesian Field Theory algorithms for low-dimensional statistical inference. SUFTware currently supports the one-dimenstional density estimation algorithm DEFT, described in , , and . The image on the right shows DEFT applied to alcohol consumption data from the World Health Organization. This computation took about 0.25 seconds on a standard laptop computer.
SUFTware can be installed from
PyPI using the
manager. At the command line:
pip install suftware
The code for SUFTware is open source and available on GitHub.
To make the figure shown above, do this from within Python:
import suftware as sw sw.demo()
For technical assistance or to report bugs, please contact Ammar Tareen.
For more general correspondence, please contact Justin Kinney.
|||Chen W, Tareen A, Kinney JB (2018) Density estimation on
small datasets. arXiv:1804.01932 [physics.data-an].
|||Kinney JB (2015) Unification of field theory and maximum
entropy methods for learning probability densities. Phys Rev E 92:032107.
|||Kinney JB (2014) Estimation of probability densities using
scale-free field theories. Phys Rev E 90:011301(R).