Object-oriented Bayesian networks. Koller, D. & Pfeffer, A. In Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence, of UAI'97, pages 302–313, Providence, Rhode Island, August, 1997. abstract bibtex 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.
@inproceedings{koller_object-oriented_1997,
address = {Providence, Rhode Island},
series = {{UAI}'97},
title = {Object-oriented {Bayesian} networks},
isbn = {978-1-55860-485-8},
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.},
urldate = {2021-11-19},
booktitle = {Proceedings of the {Thirteenth} conference on {Uncertainty} in artificial intelligence},
author = {Koller, Daphne and Pfeffer, Avi},
month = aug,
year = {1997},
pages = {302--313},
}
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