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Ranking the Impact of Different Tests on a Hypothesis in a Bayesian Network

Schneps, L; Overill, R; Lagnado, D; (2018) Ranking the Impact of Different Tests on a Hypothesis in a Bayesian Network. Entropy , 20 (11) , Article 856. 10.3390/e20110856. Green open access

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Abstract

Testing of evidence in criminal cases can be limited by temporal or financial constraints or by the fact that certain tests may be mutually exclusive, so choosing the tests that will have maximal impact on the final result is essential. In this paper, we assume that a main hypothesis, evidence for it and possible tests for existence of this evidence are represented in the form of a Bayesian network, and use three different methods to measure the impact of a test on the main hypothesis. We illustrate the methods by applying them to an actual digital crime case provided by the Hong Kong police. We conclude that the Kullback-Leibler divergence is the optimal method for selecting the tests with the highest impact.

Type: Article
Title: Ranking the Impact of Different Tests on a Hypothesis in a Bayesian Network
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/e20110856
Publisher version: https://doi.org/10.3390/e20110856
Language: English
Additional information: Copyright © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Bayesian networks; impact measures; Kullback–Leibler divergence; tornado method
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Experimental Psychology
URI: https://discovery.ucl.ac.uk/id/eprint/10064443
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