University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Conditional probability generation methods for high reliability effects-based decision making

Garn, W and Louvieris, P (2016) Conditional probability generation methods for high reliability effects-based decision making arXiv (Comput), arXiv:.

[img]
Preview
Text
Garn_2015_CPT_Generation_Methods_arXiv_1512.08553v1.pdf - ["content_typename_Accepted version (post-print)" not defined]
Available under License : See the attached licence file.

Download (200kB) | Preview
[img]
Preview
PDF (licence)
SRI_deposit_agreement.pdf
Available under License : See the attached licence file.

Download (33kB) | Preview

Abstract

Decision making is often based on Bayesian networks. The building blocks for Bayesian networks are its conditional probability tables (CPTs). These tables are obtained by parameter estimation methods, or they are elicited from subject matter experts (SME). Some of these knowledge representations are insufficient approximations. Using knowledge fusion of cause and effect observations lead to better predictive decisions. We propose three new methods to generate CPTs, which even work when only soft evidence is provided. The first two are novel ways of mapping conditional expectations to the probability space. The third is a column extraction method, which obtains CPTs from nonlinear functions such as the multinomial logistic regression. Case studies on military effects and burnt forest desertification have demonstrated that so derived CPTs have highly reliable predictive power, including superiority over the CPTs obtained from SMEs. In this context, new quality measures for determining the goodness of a CPT and for comparing CPTs with each other have been introduced. The predictive power and enhanced reliability of decision making based on the novel CPT generation methods presented in this paper have been confirmed and validated within the context of the case studies.

Item Type: Article
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
AuthorsEmailORCID
Garn, WUNSPECIFIEDUNSPECIFIED
Louvieris, PUNSPECIFIEDUNSPECIFIED
Date : 28 December 2016
Additional Information : This is an arXiv version of the paper.
Depositing User : Symplectic Elements
Date Deposited : 06 Jan 2016 12:46
Last Modified : 06 Jan 2016 12:46
URI: http://epubs.surrey.ac.uk/id/eprint/809680

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800