# SUFTware¶

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 [1], [2], and [3]. 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.

Code for this and other examples can be found on the Examples page. The Tutorial page contains a short tutorial on how to use SUFTware. The Documentation page details the SUFTware API.

## Installation¶

SUFTware can be installed from PyPI using the pip package manager (version 9.0.0 or higher). At the command line:

pip install suftware


The code for SUFTware is open source and available on GitHub.

## Quick Start¶

To make the figure shown above, do this from within Python:

import suftware as sw
sw.demo()


## Contact¶

For technical assistance or to report bugs, please contact Ammar Tareen.

For more general correspondence, please contact Justin Kinney.

Other links:

## References¶

 [1] Chen W, Tareen A, Kinney JB (2018) Density estimation on small datasets. arXiv:1804.01932 [physics.data-an].
 [2] Kinney JB (2015) Unification of field theory and maximum entropy methods for learning probability densities. Phys Rev E 92:032107. PDF.
 [3] Kinney JB (2014) Estimation of probability densities using scale-free field theories. Phys Rev E 90:011301(R). PDF.