# 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. 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()
```

## Resources¶

## 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].
`PDF` . |

[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` . |