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Daphne Koller Publications

Object-Oriented Bayesian Networks (1997)

by D. Koller and A. Pfeffer

Abstract: Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applications. However, when faced with a large complex domain, the task of modeling using Bayesian networks begins to resemble the task of programming using logical circuits. In this paper, we describe an object-oriented Bayesian network (OOBN) language, which allows complex domains to be described in terms of inter-related objects. We use a Bayesian network fragment to describe the probabilistic relations between the attributes of an object. These attributes can themselves be objects, providing a natural framework for encoding part-of hierarchies. Classes are used to provide a reusable probabilistic model which can be applied to multiple similar objects. Classes also support inheritance of model fragments from a class to a subclass, allowing the common aspects of related classes to be defined only once. Our language has clear declarative semantics: an OOBN can be interpreted as a stochastic functional program, so that it uniquely specifies a probabilistic model. We provide an inference algorithm for OOBNs, and show that much of the structural information encoded by an OOBN-particularly the encapsulation of variables within an object and the reuse of model fragments in different contexts-can also be used to speed up the inference process.

Download Information

D. Koller and A. Pfeffer (1997). "Object-Oriented Bayesian Networks." Proceedings of the 13th Annual Conference on Uncertainty in AI (UAI) (pp. 302-313). Winner of the Best Student Paper Award. pdf ps.gz

Bibtex citation

  author =       "D.~Koller and A.~Pfeffer",
  booktitle =    "Proceedings of the 13th Annual Conference on Uncertainty in AI (UAI)", 
  title =        "Object-Oriented {B}ayesian Networks",
  pages =        "302--313",
  year =         "1997",
  Note = {Winner of the Best Student Paper Award},

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