iOOBN: A Bayesian Network Modelling Tool Using Object Oriented Bayesian Networks with Inheritance
Md. Samiullah, Thao Xuan Hoang, David W. Albrecht, Ann E. Nicholson, Kevin B. Korb · 2017
The construction of Bayesian Networks (BNs) to model large-scale real-life problems is challenging. One approach to scaling up is Object Oriented Bayesian Networks (OOBNs). These provide modellers with the ability to define classes and construct models with a compositional and hierarchical structure, enabling reuse and supporting maintenance. In the OO programming paradigm, a key concept is inheritance, the ability to derive attributes and behavior from pre-existing classes, which enables an even higher level of reusability and scalability. However, inheritance in OOBNs has yet to be fully defined and implemented. Here we present iOOBN, a tool which provides fully defined inheritance for OOBNs. We provide guidance on modelling in iOOBN, describe our prototype implementation with an existing BN software tool, Hugin, and demonstrate its applicability and usefulness via a case study of re-engineering an existing large complex dynamic OOBN.