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Feature Tour


Model Utilization

BayesiaLab provides a range of functions for systematically utilizing the knowledge contained in a Bayesian network. They make a network accessible as an expert system that can be queried interactively by an end user or through an automated process.

The  Adaptive Questionnaire function provides guidance in terms of 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 variable, 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). The Adaptive Questionnaire will be presented in the context of an example about tumor classification in Chapter 6.

The WebSimulator is a platform for publishing interactive models and Adaptive Questionnaires via the web, which means that any Bayesian network model 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. Click here to try out the WebSimulator.


Batch Inference is available for automatically performing inference on a large number of 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 Export function 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, and JavaScript.

Developers can also access many of BayesiaLab’s functionsoutside the graphical user interfaceby using the Bayesia Engine APIs. The Bayesia Modeling Engine allows constructing and editing 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. The Bayesia Engine APIs are implemented as pure Java class libraries (jar files), which can be integrated into any software project.


5th Annual BayesiaLab Conference