1997 ObjectOrientedBayesianNetworks

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Subject Headings: Bayesian Network Model.

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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.

2 The object-oriented framework

2.1 Objects and types

Definition 2.2: A structured type is a set of values defined by a tuple ..., where … are attribute labels and … are corresponding (basic or structured) types. The set of values of this type are all those of the form … A structured variable is a variable which takes values in some structured type.,


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
1997 ObjectOrientedBayesianNetworksDaphne Koller
Avi Pfeffer
Object-Oriented Bayesian NetworksProceedings of UAIhttp://ai.stanford.edu/~koller/Papers/Koller+Pfeffer:UAI97.pdf1997