The Bayes’ theorem can be generalized to account for uncertainty on reported evidence. This has an impact on the value of the evidence, making the computation of the Bayes factor more demanding, as discussed by Taroni, Garbolino, and Bozza (2020). Probabilistic graphical models can however represent a suitable tool to assist the scientist in their evaluative task. A Bayesian network is proposed to deal with equivocal evidence and its use is illustrated through examples.
A probabilistic graphical model for assessing equivocal evidence
Garbolino Paolo;Bozza S
2024-01-01
Abstract
The Bayes’ theorem can be generalized to account for uncertainty on reported evidence. This has an impact on the value of the evidence, making the computation of the Bayes factor more demanding, as discussed by Taroni, Garbolino, and Bozza (2020). Probabilistic graphical models can however represent a suitable tool to assist the scientist in their evaluative task. A Bayesian network is proposed to deal with equivocal evidence and its use is illustrated through examples.
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