Skip to content
/ jbayes Public

Simple Bayesian Belief Network inference library using approximate and exact methods for Java.

License

Notifications You must be signed in to change notification settings

vangj/jbayes

Repository files navigation

logo

jbayes

Simple Java Bayesian Belief Network (BBN) inference library using likelihood weight sampling for approximate inference and the junction tree algorithm for exact inference.

Part of this library is a port of jsbayes. Another related JavaScript project that provides visualization and interaction with BBN is jsbayes-viz.

How do i use jbayes?

Using Maven, you make a depedency to jbayes.

<dependency>
  <groupId>com.github.vangj</groupId>
  <artifactId>jbayes-inference</artifactId>
  <version>0.0.3</version>
</dependency>

For construction of a BBN and performing approximate inference, please read APPROXIMATE-INFERENCE.md.

For construction of a BBN and performing exact inference, please read EXACT-INFERENCE.md.

Maven Repositories

Python

But I like using Python, how may I use Bayesian Belief Networks in Python?

There is a Python port available.

Copyright

Software

Copyright 2016 Jee Vang

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Art Copyright

Copyright 2020 Daytchia Vang

Citation

@misc{vang_2016, 
title={jbayes}, 
url={https://github.com/vangj/jbayes/}, 
journal={GitHub},
author={Vang, Jee}, 
year={2016}, 
month={Apr}}

Sponsor, Love

About

Simple Bayesian Belief Network inference library using approximate and exact methods for Java.

Resources

License

Stars

Watchers

Forks

Packages

No packages published