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Compositional Bayesian modelling for computation of evidence collection strategies

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Abstract

As forensic science and forensic statistics become increasingly sophisticated, and judges and juries demand more timely delivery of more convincing scientific evidence, crime investigation is becoming progressively more challenging. In particular, this development requires more effective and efficient evidence collection strategies, which are likely to produce the most conclusive information with limited available resources. Evidence collection is a difficult task, however, because it necessitates consideration of: a wide range of plausible crime scenarios, the evidence that may be produced under these hypothetical scenarios, and the investigative techniques that can recover and interpret the plausible pieces of evidence. A knowledge based system (KBS) can help crime investigators by retrieving and reasoning with such knowledge, provided that the KBS is sufficiently versatile to infer and analyse a wide range of plausible scenarios. This paper presents such a KBS. It employs a novel compositional modelling technique that is integrated into a Bayesian model based diagnostic system. These theoretical developments are illustrated by a realistic example of serious crime investigation.

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Authors and Affiliations

  1. Department of Computer Science, King’s College London, Strand, London, WC2R 2LS, UK

    Jeroen Keppens

  2. Department of Computer Science, Aberystwyth University, Penglais Campus, Aberystwyth, SY23 3DB, UK

    Qiang Shen & Chris Price

Authors
  1. Jeroen Keppens
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  2. Qiang Shen
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  3. Chris Price
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Correspondence to Qiang Shen.

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Keppens, J., Shen, Q. & Price, C. Compositional Bayesian modelling for computation of evidence collection strategies. Appl Intell 35, 134–161 (2011). https://doi.org/10.1007/s10489-009-0208-5

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