BNet.News
In This Issue: November/December 2006 
•   Charles River Analytics Uses BNet.Builder in an Early Warning System for Disease Outbreaks
•   BNet.EngineKit User Tip: Documentation
•   View Back Issues of BNet.News
•   BNet.Builder User Tip: Documentation
•   Publicize Your Belief Network Success Story
Charles River Analytics Uses BNet.Builder in an Early Warning System for Disease Outbreaks
CAMBRIDGE, MA – With terrorist actions such as the anthrax delivery through the US Postal Service, there is an urgent need for an early warning system for disease outbreaks. Charles River Analytics has created the Automated Link Analysis for Data-mining of Distributed Information (ALADDIN), a system employing a Bayesian Belief Network approach for such early detection.

Belief networks are powerful modeling tools for condensing what is known about causes and effects into a compact network of probabilities. ALADDIN uses Charles River Analytics’s Bayesian Belief Network engine, BNet.Builder.

The technical approach of ALADDIN is to fuse data from multiple sources and generate alerts. It discovers non-obvious connections between entities to further establish evidence of a potential attack. ALADDIN employs a model to deal with the heterogeneous nature of data sources. It has validated its approach by simulating bioterrorist attacks in model cities.

To read more about Belief Networks and the BNet product family, click here.

BNet.Builder User Tip: Documentation
BNet.Builder includes three useful documents to help you. You can access them in the Help menu.

Quick Start Guide: covers the basics of editing & using Bayesian networks.

User's Guide: explains in detail of all of BNet.Builder's features.

Overview of Bayesian Belief Networks: provides a background on the theory & math of Bayesian networks

To learn more about BNet.Builder 1.4, click here.

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BNet.EngineKit User Tip: Documentation
BNet.EngineKit includes two useful forms of documentation to help you. You can find them in the docs directory.

Developer's Guide: contains task-oriented descriptions of the BNet.EngineKit API.

Javadocs: complete API reference detailing all of the classes & methods available in BNet.EngineKit.

To learn more about Engine.Kit, click here.

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View Back Issues of BNet.News
We have backissues of BNet.News posted on our Web site. If you're a new subscriber or are looking for more articles and tips, come visit our Web site.

To visit the BNet.News archive, click here.

Publicize Your Belief Network Success Story
Want to show off your applications of belief networks to others using them in their research? Send us your success stories. We would like to include at least one in each edition of BNet.News.

To send us your story, please contact lcordeiro@cra.com or zcox@cra.com.

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