bayesian networks and probabilistic inference in forensic science pdf

Bayesian networks and probabilistic inference in forensic science pdf

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Sensitivity Analysis of Bayesian Networks Used in Forensic Investigations

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science 2e

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Wiley Online Library Sample Chapter. Ian Evett, Principal Forensic Services Ltd, London, UK Continuing developments in science and technology mean that theamounts of information forensic scientists are able to provide forcriminal investigations is ever increasing. The commensurate increase in complexity creates difficulties forscientists and lawyers with regard to evaluation andinterpretation, notably with respect to issues of inference anddecision. Probability theory, implemented through graphical methods, andspecifically Bayesian networks, provides powerful methods to dealwith this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to thejudicial system. Bayesian Networks for Probabilistic Inference and DecisionAnalysis in Forensic Science provides a unique and comprehensiveintroduction to the use of Bayesian decision networks for theevaluation and interpretation of scientific findings in forensicscience, and for the support of decision-makers in their scientificand legal tasks. The clear and accessible style of this second edition makes thisbook ideal for all forensic scientists, applied statisticians andgraduate students wishing to evaluate forensic findings from theperspective of probability and decision analysis.

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! The analysis demonstrates that the conclusions drawn from Bayesian network models are statistically reliable and stable for small changes in evidence likelihood values. Skip to main content Skip to sections. This service is more advanced with JavaScript available.

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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! The analysis demonstrates that the conclusions drawn from Bayesian network models are statistically reliable and stable for small changes in evidence likelihood values. Skip to main content Skip to sections.


The amount of information forensic scientists are able to offer is ever increasing, owing to vast developments in science and technology.


Sensitivity Analysis of Bayesian Networks Used in Forensic Investigations

David J. The book opens with several introductory chapters on probability, Bayesian networks BNs and basic principles of evidence evaluation. There follow chapters on DNA evidence and transfer evidence such as fibres. The final chapters cover some more advanced general topics such as combinations of evidence, sensitivity analysis and qualitative and continuous networks. The level of discussion is reasonably elementary and at a leisurely pace, allowing an interested reader with little mathematical training to follow the arguments.

Metrics details. When new forensic evidence becomes available after a conviction there is no systematic framework to help lawyers to determine whether it raises sufficient questions about the verdict in order to launch an appeal. This paper presents such a framework driven by a recent case, in which a defendant was convicted primarily on the basis of audio evidence, but where subsequent analysis of the evidence revealed additional sounds that were not considered during the trial. The framework is intended to overcome the gap between what is generally known from scientific analyses and what is hypothesized in a legal setting. It is based on Bayesian networks BNs which have the potential to be a structured and understandable way to evaluate the evidence in a specific case context.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science 2e

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Sensitivity Analysis of Bayesian Networks Used in Forensic Investigations

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