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Explaining inference on a population...

Sutovsky, Peter.

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Explaining inference on a population of independent agents using Bayesian networks.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Explaining inference on a population of independent agents using Bayesian networks./
Author:	Sutovsky, Peter.
Description:	149 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.
Contained By:	Dissertation Abstracts International75-03B(E).
Subject:	Information Technology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3577049
ISBN:	9781303591518

Explaining inference on a population of independent agents using Bayesian networks.
Sutovsky, Peter.

Explaining inference on a population of independent agents using Bayesian networks. - 149 p.

Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.

Thesis (Ph.D.)--University of Pittsburgh, 2013.

The main goal of this research is to design, implement, and evaluate a novel explanation method, the hierarchical explanation method (HEM), for explaining Bayesian network (BN) inference when the network is modeling a population of conditionally independent agents, each of which is modeled as a subnetwork. For example, consider disease-outbreak detection in which the agents are patients who are modeled as independent, conditioned on the factors that cause disease spread. Given evidence about these patients, such as their symptoms, suppose that the BN system infers that a respiratory anthrax outbreak is highly likely. A public-health official who received such a report would generally want to know why anthrax is being given a high posterior probability. The HEM explains such inferences. The explanation approach is applicable in general to inference on BNs that model conditionally independent agents; it complements previous approaches for explaining inference on BNs that model a single agent (e.g., for explaining the diagnostic inference for a single patient using a BN that models just that patient). The hypotheses that were tested are: (1) the proposed explanation method provides information that helps a user to understand how and why the inference results have been obtained, (2) the proposed explanation method helps to improve the quality of the inferences that users draw from evidence.

ISBN: 9781303591518Subjects--Topical Terms:

1030799
Information Technology.

Explaining inference on a population of independent agents using Bayesian networks.
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020 $a 9781303591518
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100 1 $a Sutovsky, Peter. $3 2072863
245 1 0 $a Explaining inference on a population of independent agents using Bayesian networks.
300 $a 149 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-03(E), Section: B.
500 $a Adviser: Gregory F. Cooper.
502 $a Thesis (Ph.D.)--University of Pittsburgh, 2013.
520 $a The main goal of this research is to design, implement, and evaluate a novel explanation method, the hierarchical explanation method (HEM), for explaining Bayesian network (BN) inference when the network is modeling a population of conditionally independent agents, each of which is modeled as a subnetwork. For example, consider disease-outbreak detection in which the agents are patients who are modeled as independent, conditioned on the factors that cause disease spread. Given evidence about these patients, such as their symptoms, suppose that the BN system infers that a respiratory anthrax outbreak is highly likely. A public-health official who received such a report would generally want to know why anthrax is being given a high posterior probability. The HEM explains such inferences. The explanation approach is applicable in general to inference on BNs that model conditionally independent agents; it complements previous approaches for explaining inference on BNs that model a single agent (e.g., for explaining the diagnostic inference for a single patient using a BN that models just that patient). The hypotheses that were tested are: (1) the proposed explanation method provides information that helps a user to understand how and why the inference results have been obtained, (2) the proposed explanation method helps to improve the quality of the inferences that users draw from evidence.
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710 2 $a University of Pittsburgh. $b Information Sciences. $3 2102250
773 0 $t Dissertation Abstracts International $g 75-03B(E).
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3577049