Bayesian Networks and Probabilistic Inference in Forensic Science

Taroni, Franco; Aitken, Colin; Garbolino, Paolo; Biedermann, Alex

doi:10.1002/0470091754

Cited by 184 publications

(168 citation statements)

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“…Since then, Bayesian networks have become popular for modelling parts of a case (see, e.g., Taroni et al 2006), but much less so for representing entire cases. Nonetheless, some work has been done to simplify the construction of a complex network for a legal case (i.e., a network that is larger than a few nodes for analysing a technical result) by using basic structures that recur throughout various cases as building blocks.…”

Section: Discussionmentioning

confidence: 99%

“…A probabilistic approach lends itself very well to analysing evidential support, which is often reported in terms of likelihood ratios (LR) (Taroni et al 2006). However, a likelihood ratio is most useful when two mutually exclusive and jointly exhaustive hypotheses are compared (Fenton et al 2014).…”

Section: Evidential Supportmentioning

confidence: 99%

“…Bayesian networks have become popular as a probabilistic tool for working with forensic evidence (see, e.g., Taroni et al 2006). A Bayesian network consists of a graph and probability tables, together representing a joint probability distribution.…”

Section: Introductionmentioning

confidence: 99%

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A method for explaining Bayesian networks for legal evidence with scenarios

et al. 2016

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In a criminal trial, a judge or jury needs to reason about what happened based on the available evidence, often including statistical evidence. While a probabilistic approach is suitable for analysing the statistical evidence, a judge or jury may be more inclined to use a narrative or argumentative approach when considering the case as a whole. In this paper we propose a combination of two approaches, combining Bayesian networks with scenarios. Whereas a Bayesian network is a popular tool for analysing parts of a case, constructing and understanding a network for an entire case is not straightforward. We propose an explanation method for understanding a Bayesian network in terms of scenarios. This method builds on a previously proposed construction method, which we slightly adapt with the use of scenario schemes for the purpose of explaining. The resulting structure is explained in terms of scenarios, scenario quality and evidential support. A probabilistic interpretation of scenario quality is provided using the concept of scenario schemes. Finally, the method is evaluated by means of a case study.

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Section: Discussionmentioning

confidence: 99%

Section: Evidential Supportmentioning

confidence: 99%

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A method for explaining Bayesian networks for legal evidence with scenarios

et al. 2016

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“…For instance in the legal or medical domain the consequences of reasoning errors can be severe. Bayesian networks, which model probability distributions, have found a number of applications in these domains (see [9] for an overview). However, the interpretation of BNs is a difficult task, especially for domain experts who are not trained in probabilistic reasoning.…”

Section: Introductionmentioning

confidence: 99%

Explaining Bayesian Networks Using Argumentation

Timmer

Meyer

Prakken

et al. 2015

Lecture Notes in Computer Science

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Abstract. Qualitative and quantitative systems to deal with uncertainty coexist. Bayesian networks are a well known tool in probabilistic reasoning. For non-statistical experts, however, Bayesian networks may be hard to interpret. Especially since the inner workings of Bayesian networks are complicated they may appear as black box models. Argumentation approaches, on the contrary, emphasise the derivation of results. Argumentation models, however, have notorious difficulty dealing with probabilities. In this paper we formalise a two-phase method to extract probabilistically supported arguments from a Bayesian network. First, from a BN we construct a support graph, and, second, given a set of observations we build arguments from that support graph. Such arguments can facilitate the correct interpretation and explanation of the evidence modelled in the Bayesian network.

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“…The application of Bayes' theorem and graph theory provides a means to characterize the causal relationships among variables [16]. In terms of forensic science, these correspond to the hypothesis and evidence.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Bayesian Networks Used in Forensic Investigations

Kwan

Overill

Chow

et al. 2011

IFIP Advances in Information and Communication Technology

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Research on using Bayesian networks to enhance digital forensic investigations has yet to evaluate the quality of the output of a Bayesian network. The evaluation can be performed by assessing the sensitivity of the posterior output of a forensic hypothesis to the input likelihood values of the digital evidence. This paper applies Bayesian sensitivity analysis techniques to a Bayesian network model for the well-known Yahoo! case. The analysis demonstrates that the conclusions drawn from Bayesian network models are statistically reliable and stable for small changes in evidence likelihood values.

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Bayesian Networks and Probabilistic Inference in Forensic Science

Cited by 184 publications

References 0 publications

A method for explaining Bayesian networks for legal evidence with scenarios

A method for explaining Bayesian networks for legal evidence with scenarios

Explaining Bayesian Networks Using Argumentation

Sensitivity Analysis of Bayesian Networks Used in Forensic Investigations

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