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Dynamic Bayesian networks

Given the complexity of the domains for which we would like to use computers as reasoning
engines, an automated reasoning process will often be required to perform under some state of uncertainty. Probability provides a normative theory with which uncertainty can be modelled. Without assumptions of independence from the domain, naive computations of probability are intractible. If probability theory is to be used effectively in AI applications,
the independence assumptions from the domain should be represented explicitly, and used to greatest possible advantage. One such representation is a class of mathematical structures called Bayesian networks.
This thesis presents a framework for dynamically constructing and evaluating Bayesian networks. In particular, this thesis investigates the issue of representing probabilistic knowledge which has been abstracted from particular individuals to which this knowledge
may apply, resulting in a simple representation language. This language makes the independence assumptions for a domain explicit.
A simple procedure is provided for building networks from knowledge expressed in this language. The mapping between the knowledge base and network created is precisely defined, so that the network always represents a consistent probability distribution.
Finally, this thesis investigates the issue of modifying the network after some evaluation has taken place, and several techniques for correcting the state of the resulting model are derived. / Science, Faculty of / Computer Science, Department of / Graduate

Identiferoai:union.ndltd.org:UBC/oai:circle.library.ubc.ca:2429/28909
Date January 1990
CreatorsHorsch, Michael C.
PublisherUniversity of British Columbia
Source SetsUniversity of British Columbia
LanguageEnglish
Detected LanguageEnglish
TypeText, Thesis/Dissertation
RightsFor non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.

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