Bayesian Network Software Free

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Bayesian Network Software Free 2015

Bayesian Network Tools in Java

Bayesian Network Tools in Java (BNJ) is an open-source suite of software tools for research and development using graphical models of probability. It is implemented in 100% pure Java and distributed under the GNU General Public License (GPL) by the Kansas State University Laboratory for Knowledge Discovery in Databases (KDD).

BN PowerConstructor

BN PowerConstructor: An efficient system that learns Bayesian belief network structures & parameters from data. This system is based on three-phase belief network construction algorthims. It includes a wizard-like user interface and a belief network construction engine.

Dynamic Bayesian Network Simulator

A simulator for learning techniques for dynamic bayesian networks.

JavaBayes

The JavaBayes system is a set of tools 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.

jBNC Toolkit

jBNC is a Java toolkit for training, testing, and applying Bayesian Network Classifiers. Implemented classifiers have been shown to perform well in a variety of artificial intelligence, machine learning, and data mining applications.

JNCC2

The Java Implementation of Naive Credal Classifier 2 (JNCC2) addresses the problems of statistical classification. NCC2 constitutes an extension of the traditional Naive Bayes Classifier (NBC) towards imprecise probabilities; it is designed to return robust classification, even on small and/or incomplete data sets.

MSBNx

Microsoft Bayesian Network Editor (MSBNx) is a component-based Windows application for creating, assessing, and evaluating Bayesian Networks.

SMILE

SMILE (Structural Modeling, Inference, and Learning Engine) is a fully portable library of C++ classes implementing graphical decision-theoretic methods, such as Bayesian networks and influence diagrams, directly amenable to inclusion in intelligent systems. Its Windows user interface, GeNIe is a versatile and user-friendly development environment for graphical decision-theoretic models.