Model Utilization

Model Utilization

Model Utilization

  • 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.

Adaptive Questionnaire

  • The Adaptive Questionnaire function provides guidance regarding the optimum sequence for seeking evidence.
  • 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 (opens in a new tab) 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).

See Examples & Learn More


  • The BayesiaLab WebSimulator is a platform for publishing interactive models and Adaptive Questionnaires via the web, which means that any Bayesian network built with BayesiaLab can be shared privately with clients or publicly with a broader audience.
  • Once a model is published via the WebSimulator, end users can try out scenarios and examine the dynamics of that model.

Code Export

  • Batch Inference is available for automatically performing inference on many records in a dataset. For example, Batch Inference can be used to produce a predictive score for all customers in a database.
  • With the same objective, BayesiaLab’s optional Code Export Module can translate predictive network models into static code that can run in external programs. Modules are available that can generate code for R, SAS, PHP, VBA, Python, and JavaScript.

Bayesia Engine API

  • Developers can also access many of BayesiaLab’s functions—outside the graphical user interface—by using the Bayesia Engine API.
  • 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.

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