Metadata-Version: 2.1
Name: simpleSVGD
Version: 0.1
Summary: A very basic implementation of SVGD, based on https://github.com/dilinwang820/Stein-Variational-Gradient-Descent
Home-page: https://github.com/larsgeb/simpleSVGD
Author: Lars Gebraad
Author-email: lars.gebraad@erdw.ethz.ch
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/larsgeb/simpleSVGD/issues
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: tqdm
Requires-Dist: scipy
Requires-Dist: matplotlib
Provides-Extra: dev
Requires-Dist: black ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'

# simpleSVGD

This package is a tiny SVGD algorithm specifically developed to operate on distributions found in [HMCLab](https://github.com/larsgeb/HMCLab).
## Stein Variational Gradient Descent (SVGD) 
SVGD is a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization. SVGD iteratively transports a set of particles to match with the target distribution, by applying a form of functional gradient descent that minimizes the KL divergence.

For more information, please visit the original implementers project website - [SVGD](http://www.cs.utexas.edu/~qlearning/project.html?p=vgd), or their publication Qiang Liu and Dilin Wang. [Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm](http://arxiv.org/abs/1608.04471). NIPS, 2016.


