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.

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