Metadata-Version: 2.0
Name: suftware
Version: 0.6a0
Summary: Statistics Using Field Theory
Home-page: http://github.com/jbkinney/suftware
Author: Wei-Chia Chen, Ammar Tareen, Justin B. Kinney
Author-email: jkinney@cshl.edu
License: MIT
Keywords: density estimation
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Dist: scipy
Requires-Dist: numpy
Requires-Dist: matplotlib

SUFTware is a lightweight Python package that provides provides fast and robust implementations of Bayesian Field Theory (BFT) methods for low-dimensional statistical inference. BFT is a grid-based approach to Bayesian nonparametric inference. By using a grid in lieu of specific stochastic processes (such as Dirichlet processes or Gaussian processes), BFT allows certain types of problems to be solved in a fully Bayesian manner without requiring any large-data approximations.

Currently, SUFTware supports a one-dimensional density estimation called DEFT. DEFT has substantial advantages over standard density estimation methods, including, including kernel density estimation and Dirichlet process mixture modeling. See [Chen et al., 2018; Kinney 2015; Kinney 2014].

Installation¶
pip install suftware
Requirements

Python >= 3.6.3
or Python = 2.7.10
numpy >= 1.13.3
scipy >= 1.0.0
matplotlib >= 2.1.0
Quick Start
import numpy as np
import suftware as sw

# Generate random data
data = np.random.randn(100)

# Perform one-dimensional density estimation using SUFTware
density = sw.Density(data)

# Visualize results
density.plot()

