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Using sensitivity analyses in Bayesian Networks to highlight the impact of data paucity and direct future analyses: a contribution to the debate on measuring and reporting the precision of likelihood ratios.
Journal article

Using sensitivity analyses in Bayesian Networks to highlight the impact of data paucity and direct future analyses: a contribution to the debate on measuring and reporting the precision of likelihood ratios.

  • Taylor D Forensic Science South Australia, 21 Divett Place, Adelaide, SA 5000, Australia; School of Biological Sciences, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia. Electronic address: Duncan.Taylor@sa.gov.au.
  • Hicks T Faculty of Law, Criminal Justice and Public Administration, School of Criminal Justice and Fondation pour la formation continue UNIL-EPFL, University of Lausanne, Lausanne-Dorigny, Switzerland.
  • Champod C Faculty of Law, Criminal Justice and Public Administration, School of Criminal Justice, University of Lausanne, Lausanne-Dorigny, Switzerland.
  • 2016-10-06
Published in:
  • Science & justice : journal of the Forensic Science Society. - 2016
Bayesian networks
Data
Likelihood ratio
Sensitivity analysis
Source level propositions
English Bayesian networks are being increasingly used to address complex questions of forensic interest. Like all probabilities, those that underlie the nodes within a network rely on structured data and knowledge. Obviously, the more structured data we have, the better. But, in real life, the numbers of experiments that can be carried out are limited. It is thus important to know if/when our knowledge is sufficient and when one needs to perform further experiments to be in a position to report the value of the observations made. To explore the impact of the amount of data that are available for assessing results, we have constructed Bayesian Networks and explored the sensitivity of the likelihood ratios to changes to the data that underlie each node. Bayesian networks are constructed and sensitivity analyses performed using freely available R libraries (gRain and BNlearn). We demonstrate how the analyses can be used to yield information about the robustness provided by the data used to inform the conditional probability table, and also how they can be used to direct further research for maximum effect. By maximum effect, we mean to contribute with the least investment to an increased robustness. In addition, the paper investigates the consequences of the sensitivity analysis to the discussion on how the evidence shall be reported for a given state of knowledge in terms of underpinning data.
Language
  • English
Open access status
closed
Identifiers
Persistent URL
https://sonar.ch/global/documents/20463
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