Metadata-Version: 2.1
Name: veroku
Version: 0.0.9
Summary: An open source library for building and performing inference with probabilistic graphical models.
Home-page: https://github.com/ejlouw/veroku
Author: EJ Louw
Author-email: ejlouw00@gmail.com
License: BSD 3-Clause License
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: jupyterlab (==2.2.1)
Requires-Dist: matplotlib (==3.0.3)
Requires-Dist: seaborn (==0.7.1)
Requires-Dist: tqdm (==4.48.1)
Requires-Dist: graphviz (==0.10.1)
Requires-Dist: Pillow (==6.0.0)
Requires-Dist: ipython (==7.5.0)
Requires-Dist: pandas (==0.23.4)
Requires-Dist: numpy (==1.16.3)
Requires-Dist: networkx (==1.11)
Requires-Dist: numdifftools (==0.9.20)
Requires-Dist: scipy (==1.2.1)
Requires-Dist: mockito (==1.1.0)

<div align="center">
  <img src="veroku_logo.png">
</div>

### Overview
<div style="text-align: justify">
Veroku is an open source library for building and performing inference with probabilistic graphical models (PGMs). PGMs
provide a framework for performing efficient probabilistic inference with very high dimensional distributions. A typical
example of a well-known type of PGM is the Kalman filter that can be used to obtain probabilistic estimates of a hidden
state of a process or system, given noisy measurements. PGMs can in principle be used for any problem that involves
uncertainty and is therefore applicable to many problems.</div> 
Veroku currently supports the following distributions:
* Multivariate Gaussian
* Multivariate Gaussian mixture
* Multivariate categorical

<div style="text-align: justify">
These distributions can be used as factors to represent a factorised distribution. These factors can be used, together
with the `cluster_graph` module to automatically create valid cluster graphs. Inference can be performed in these graphs
using message passing algorithms. Currently only the LBU (Loopy Belief Update) message-passing algorithm is supported.
</div>

<br/><br/>

### Future Features
To be added soon:
* Example notebooks
* Non-linear Gaussian distribution
* Plate models (for efficiently specifying PGMs as modular/hierarchical templates)

On the roadmap:
* Dirichlet distribution
* Wishart distribution
* Normal-Wishart distribution



