Bayesian Network, also known as Bayes network is a probabilistic directed acyclic graphical model, which can be used for time series prediction, anomaly detection, diagnostics and more.

In machine learning, the Bayesian inference is known for its robust set of tools for modelling any random variable, including the business performance indicators, the value of a regression parameter, among others. This method is also known as one of the best approaches to modelling uncertainty.  

In this article, we list down the top eight open-source tools for Bayesian Networks.

(The list is in alphabetical order).

1| BUGS

Bayesian inference Using Gibbs Sampling or BUGS is a software package for the Bayesian analysis of statistical models by utilising the Markov chain Monte Carlo techniques. The tool carries a system that determines an appropriate Markov chain Monte Carlo scheme, which is based on the Gibbs sampler for analysing the designated model.

There are two main versions of BUGS – WinBUGS and OpenBUGS. While OpenBUGS represents the future of the BUGS project, WinBUGS is an established and stable, stand-alone version of the software, which remains available but is not further developed. 

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2| BNFinder

BNFinder or Bayes Net Finder is an open-source tool for learning Bayesian networks written purely in Python. The BNF script is the main part of BNfinder command-line tools. It is used for learning the Bayesian network from data and can be executed by typing bnf <options>. It can be used for both dynamic and static networks.

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3| bnlearn 

bnlearn is an R package for learning the graphical structure of Bayesian networks, estimate their parameters and perform some useful inference. It includes various algorithms for learning the structure of Bayesian networks with discrete as well as continuous variables. 

bnlearn implements several constraint-based structure learning algorithms, including Fast Incremental Association (Fast-IAMB), Incremental Association Markov Blanket (IAMB), Hybrid Parents & Children (HPC), among others. 

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4| Banjo

Banjo is a software application and framework written to comply with Java 5 for structure learning of static and dynamic Bayesian networks. The tool is mainly designed to provide efficient structure inference when analysing research-oriented data sets. Banjo focuses on score-based structure inference, which is a plethora of code that already exists for variable inference within a Bayesian network of known structure.

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5| Free-BN

Free-BN or FBN is an open-source Bayesian network structure learning API licensed under the Apache 2.0 license. This tool is meant for constraint-based structural learning of Bayesian networks. The features of FBN include structural learning, exact inference and logic sampling.

The FBN API is dependent on two other minor projects called Free-Display and Free-GA (FGA). While Free-Display API is used to visualise the Bayesian networks, FGA API is used for search-and-scoring methods for Bayesian network structure learning. 

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6| jBNC  

jBNC is a library of Java classes for training and testing Bayesian network classifiers. This Java toolkit is mainly used for training, testing, and applying Bayesian Network Classifiers. The classifiers implemented by jBNC have been shown to perform well in a variety of artificial intelligence, machine learning, and data mining applications. 

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7| JavaBayes

It is a set of tools used for the creation and manipulation of Bayesian networks. The system is composed of a graphical editor, a core inference engine and a set of parsers. The graphical editor allows a user to create and modify Bayesian networks in a friendly interface, while the parsers allow a user to import Bayesian networks in a variety of formats.  

Also, the core inference engine is responsible for manipulating the data structures that represent Bayesian networks and can produce marginal probability for any variable in a Bayesian network.

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8| UnBBayes

UnBBayes is a probabilistic network framework written in Java language that has both a GUI and an API with inference, sampling, learning and evaluation. The framework supports Bayesian networks, influence diagrams, MSBN, HBN, PRM, structure, parameter and incremental learning, among others.

Some other features of UnBBayes include decision graphs, approximate inference, Noisy-Max distribution, plug-in support, data mining and more.
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