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ENS Special Event - "Big data and structured patterns" & "Bayesian networks in crime evidence analysis"

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If you have a question about this talk, please contact Kasia Warburton.

The Emmy Noether Society are delighted to announce our special penultimate event of term:

Thursday, 24th November, at the Isaac Newton Institute at 4pm A double talk event followed by complimentary wine reception

Professor Sofia Olhede (University college London) & Dr Leila Schneps (Pierre and Marie Curie University, Author of ‘Maths on Trial’)

Professor Sofia Olhede: Big data and structured patterns

Scientists try to understand apparent patterns and structures from data. This is complicated because patterns can be deceiving; we are apt to see structure in noise. I will talk about building statistical models to understand data and how we can model phenomena in time and space, as well as extract model parameters for such data from observations.

Dr Leila Schneps: Bayesian networks in crime evidence analysis

One of the most difficult tasks for juries in criminal trials is to properly weigh the impact of different pieces or different kinds of evidence which may be interdependent on each other. Studies have shown that there are several fallacies that jury members fall into on a regular basis: overestimation of the weight of DNA evidence but underestimation of other types of scientific evidence, double-counting evidence, for instance taking the fact of being arrested as evidence in favor of guilt as well as the reasons for which the person was arrested, and the famous prosecutor’s fallacy or transposed conditional. Bayesian networks are a powerful tool for assessing the weight of different kinds of evidence correctly, taking all their dependencies into account. We believe that it has the potential to become an invaluable tool in investigating the real implications of crime evidence. In this talk we will explain how these networks function and give examples of their use in real cases.

This talk is part of the Emmy Noether Society series.

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