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Learning structure and parameters of...

Lauria, Eitel Jose Maria.

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Learning structure and parameters of Bayesian belief networks: An application and a methodology in information technology implementation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Learning structure and parameters of Bayesian belief networks: An application and a methodology in information technology implementation./
作者:	Lauria, Eitel Jose Maria.
面頁冊數:	227 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-09, Section: A, page: 3118.
Contained By:	Dissertation Abstracts International64-09A.
標題:	Information Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3107154
ISBN:	049654716X

Learning structure and parameters of Bayesian belief networks: An application and a methodology in information technology implementation.
Lauria, Eitel Jose Maria.

Learning structure and parameters of Bayesian belief networks: An application and a methodology in information technology implementation. - 227 p.

Source: Dissertation Abstracts International, Volume: 64-09, Section: A, page: 3118.

Thesis (Ph.D.)--State University of New York at Albany, 2003.

A Bayesian belief network (BBN) encodes the probability distribution of a set of random variables by specifying a set of conditional independence assumptions together with a set of relationships among these variables and their related joint probabilities. A key feature of BBNs is that they enable us to model and reason about uncertainty, by providing a graphical representation that can help articulate expert beliefs about the dependencies between different variables and expose some of the common pitfalls in reasoning due to misunderstanding of probability. Also, they play an increasingly important role in the design and analysis of machine learning algorithms, constituting an innovative way of approaching problems related to artificial intelligence.

ISBN: 049654716XSubjects--Topical Terms:

1017528
Information Science.

Learning structure and parameters of Bayesian belief networks: An application and a methodology in information technology implementation.
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245 1 0 $a Learning structure and parameters of Bayesian belief networks: An application and a methodology in information technology implementation.
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500 $a Source: Dissertation Abstracts International, Volume: 64-09, Section: A, page: 3118.
500 $a Adviser: Carlos C. Rodriguez.
502 $a Thesis (Ph.D.)--State University of New York at Albany, 2003.
520 $a A Bayesian belief network (BBN) encodes the probability distribution of a set of random variables by specifying a set of conditional independence assumptions together with a set of relationships among these variables and their related joint probabilities. A key feature of BBNs is that they enable us to model and reason about uncertainty, by providing a graphical representation that can help articulate expert beliefs about the dependencies between different variables and expose some of the common pitfalls in reasoning due to misunderstanding of probability. Also, they play an increasingly important role in the design and analysis of machine learning algorithms, constituting an innovative way of approaching problems related to artificial intelligence.
520 $a This dissertation addresses the issue of applying Bayesian networks in the domain of information technology implementation, aimed towards building a probabilistic expert system capable of predicting/explaining the outcomes of a technology implementation process on the basis of evidence elicited from the organization under analysis. The underlying Bayesian network is the result of a machine learning process that renders the structure of the network and its parameters. The study presents a methodology for efficiently learning the structure and parameters of the model from collected data. The research combines (a) the use of heuristic methods to search the space of network structures; (b) the use of a scoring function that refines the approach of the Bayesian Information Criteria, drawn from research that has lead to the development of an information-geometric (info-geo) complexity measure based on the updated version of the Minimum Description Length Principle (Rissanen 1996), that takes into account the geometry of the Riemannian manifold that maps the parametric family of probability distributions of a given model (Amari 1985, Rodriguez 1989).
520 $a Additionally, this work provides a contribution to the field of Bayesian belief networks by clarifying the underlying properties of the info-geo scoring metric and its usefulness when applied to the problem of learning the structure and parameters of a Bayesian network from data.
590 $a School code: 0668.
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