Features & Benefits
Be an expert in your field, not an expert in Bayesian network software
BNet.EngineKit is a developer toolkit for researchers and engineers to use to embed belief networks in software applications.
With BNet.EngineKit’s utilities, documents and sample code, you can implement your belief networks in your projects easily and simply, and make a difference with your work quickly.
Clear, concise documentation for quick implementation
- Developer Manual with example implementations and sample code – including:
- High-level summaries and descriptions of available functions
- Activity-based examples with sample code
- Detailed Javadocs with instructions on the nuts and bolts making code work
Beliefs & Evidence Tools – Building the code to get beliefs out and enter evidence into a network are the most common and most time consuming tasks in building applications that use belief networks. Speed up your implementation with:
- Belief Alerts–
- Automated notification when beliefs at any node are updated
- Alerts that can be set to trigger on a wide variety of node conditions, including belief thresholds and ranges, node names, state names and more
- Evidence Discretizer set ranges to convert continuous variables into hard evidence
- Evidence Fuzzifier set ranges to convert continuous variables into soft evidence
- Utility Methods for common tasks like getting evidence and beliefs, posting evidence, and more
Fast Inference – Charles River Analytics’ proprietary inference engine, available only in BNet products, is one of the fastest belief network inference engines available
Multithreading Support – Thread-compatible engine and documentation to help you implement multithreading in your projects
Learning – BNet.EngineKit can learn your CPTs from data in both fully and partially observed Bayesian networks
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