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A knowledge representation scheme fo...

Tao, Lin.

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A knowledge representation scheme for the Bayesian network model.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A knowledge representation scheme for the Bayesian network model./
Author:	Tao, Lin.
Description:	122 p.
Notes:	Source: Dissertation Abstracts International, Volume: 65-10, Section: B, page: 5241.
Contained By:	Dissertation Abstracts International65-10B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ96111
ISBN:	0612961117

A knowledge representation scheme for the Bayesian network model.
Tao, Lin.

A knowledge representation scheme for the Bayesian network model. - 122 p.

Source: Dissertation Abstracts International, Volume: 65-10, Section: B, page: 5241.

Thesis (Ph.D.)--The University of Regina (Canada), 2004.

The Bayesian network model forms the basis for probabilistic reasoning in this exposition. A Bayesian network consists of a directed acyclic graph, and a set of conditional probability tables. The directed acyclic graph encodes the conditional independencies of the domain variables. The product of the conditional probability tables defines a joint probability distribution representing the domain knowledge. In order to build a Bayesian network, one may construct the directed acyclic graph based on the causal relationships of the variables involved. However, in many applications it may be necessary to construct the directed acyclic graph from the conditional independency information supplied by the experts.

ISBN: 0612961117Subjects--Topical Terms:

626642
Computer Science.

A knowledge representation scheme for the Bayesian network model.
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020 $a 0612961117
035 $a (UnM)AAINQ96111
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040 $a UnM $c UnM
100 1 $a Tao, Lin. $3 1288288
245 1 2 $a A knowledge representation scheme for the Bayesian network model.
300 $a 122 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-10, Section: B, page: 5241.
500 $a Adviser: S. K. Michael Wong.
502 $a Thesis (Ph.D.)--The University of Regina (Canada), 2004.
520 $a The Bayesian network model forms the basis for probabilistic reasoning in this exposition. A Bayesian network consists of a directed acyclic graph, and a set of conditional probability tables. The directed acyclic graph encodes the conditional independencies of the domain variables. The product of the conditional probability tables defines a joint probability distribution representing the domain knowledge. In order to build a Bayesian network, one may construct the directed acyclic graph based on the causal relationships of the variables involved. However, in many applications it may be necessary to construct the directed acyclic graph from the conditional independency information supplied by the experts.
520 $a This thesis suggests a hierarchical characterization of input conditional independencies that can be faithfully represented by a Bayesian network. This characterization leads to an alternative representation of Bayesian networks. Methods have been developed to construct suitable hierarchical covers for an input set of conditional independencies. These covers can be represented by an alternative graphical structure defined by a hierarchical set of acyclic hypergraphs. Probabilistic inference can be directly performed using these hypergraphs without the need to first convert them into a secondary structure as in conventional Bayesian networks. Moreover, a method is suggested to compute the marginals of the proposed representation such that all the input independency information is preserved for probabilistic reasoning.
590 $a School code: 0148.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 0 $a The University of Regina (Canada). $3 1017617
773 0 $t Dissertation Abstracts International $g 65-10B.
790 1 0 $a Wong, S. K. Michael, $e advisor
790 $a 0148
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ96111