- BayesiaLab provides a range of functions for systematically utilizing the knowledge contained in a Bayesian network. They make a Bayesian network accessible as a probabilistic expert system that can be queried interactively by an end-user.
- BayesiaLab determines dynamically, given the evidence already gathered, the next best piece of evidence to obtain in order to maximize the information gain with respect to the Target Node while minimizing the cost of acquiring such evidence.
- In a medical context, for instance, this would allow for the optimal “escalation” of diagnostic procedures from “low-cost/small-gain” evidence (e.g., measuring the patient’s blood pressure) to “high-cost/large-gain” evidence (e.g., performing an MRI scan).
- Once a model is published via the WebSimulator, end users can try out scenarios and examine the dynamics of that model.
- The Bayesia Modeling Engine allows you to construct and edit networks.
- The Bayesia Inference Engine can access network models programmatically for performing automated inference, e.g., as part of a real-time application with streaming data.
- Finally, the Bayesia Learning Engine gives you programmatic access to BayesiaLab's discretization and learning algorithms.
- The Bayesia Engine APIs are implemented as pure Java class libraries (jar files), which can be integrated into any software project.