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

Duncan Taylor, Tacha Hicks, Christophe Champod

    Research output: Contribution to journalArticlepeer-review

    22 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)402-410
    Number of pages9
    JournalScience and Justice
    Volume56
    Issue number5
    DOIs
    Publication statusPublished - 2016

    Fingerprint Dive into the research topics of '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'. Together they form a unique fingerprint.

    Cite this