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Jill Dando Institute of Crime Science

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Handling manipulated evidence

Bayesian Networks have been advocated as a useful tool to describe the relations of dependence/independence among random variables and relevant hypotheses in a crime case. Moreover, they have been applied to help the investigator structure the problem and weight the observed evidence, typically with respect to the hypothesis of guilt of a suspect. I shall describe a model, based on causal inference models, to handle the possibility that one or more pieces of evidence have been manipulated in order to mislead the investigations.

Speaker

Gianluca Baio is a Research Fellow in the Evidence program, and a Honorary Lecturer in the Department of Public Health and Policy, London School of Hygiene & Tropical Medicine. He has got a PhD in Applied Statistics from the University of Florence (Italy). His main interests are in Causal Inference using Probabilistic Expert Systems and Bayesian Networks with applications to Forensic Science, and in applied Bayesian Statistics in economic evaluation of health systems and for pharmacoeconomics analysis.