Charles River Analytics today announced the launch of its new Bayesian belief network modeling tool, BNet.EngineKit 1.0. BNet.EngineKit is a developer toolkit for researchers and engineers to use to embed belief networks in software applications. Unique in its focus on clear APIs with the right functionality, BNet.EngineKit offers software developers who are not inference algorithm specialists a chance to use Bayesian networks without spending years learning about them.
Zach Cox, software engineer and chief developer of BNet.EngineKit and BNet.Builder, notes, “EngineKit 1.0 includes learning functionality that allows you to learn your CPTs from data. Two easy-to-use algorithms learn CPTs when you have data for all nodes in your network (fully observed) and when you have missing data or hidden nodes (partially observed). The learning algorithms use a standard interface for obtaining data, so you can also create your own implementation and provide data from any custom source.”
BNet.EngineKit is part of Charles River Analytics’ family of Bayesian Network products, including BNet.Builder.