4. Point Estimation

  1. Franco Taroni1,
  2. Silvia Bozza2,
  3. Alex Biedermann1,
  4. Paolo Garbolino3 and
  5. Colin Aitken4

Published Online: 9 APR 2010

DOI: 10.1002/9780470665084.ch4

Data Analysis in Forensic Science: A Bayesian Decision Perspective

Data Analysis in Forensic Science: A Bayesian Decision Perspective

How to Cite

Taroni, F., Bozza, S., Biedermann, A., Garbolino, P. and Aitken, C. (2010) Point Estimation, in Data Analysis in Forensic Science: A Bayesian Decision Perspective, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470665084.ch4

Author Information

  1. 1

    School of Criminal Justice, University of Lausanne, Switzerland

  2. 2

    Department of Statistics, University Ca' Foscari, Venice, Italy

  3. 3

    Faculty of Arts and Design, IUAV University, Venice, Italy

  4. 4

    School of Mathematics, University of Edinburgh, UK

Publication History

  1. Published Online: 9 APR 2010
  2. Published Print: 9 APR 2010

ISBN Information

Print ISBN: 9780470998359

Online ISBN: 9780470665084

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Keywords:

  • Bayesian decision;
  • normal mean;
  • point estimation;
  • Poisson mean

Summary

This chapter illustrates how forensic scientists can ‘learn’ from past experience to draw inferences about the value of a population parameter. It covers the topic of multi-parameter (vector-valued parameter) learning. It is a common need in forensic practice to obtain an idea of the proportion of individuals or items that share a given characteristic. The chapter addresses this topic through a Bayesian decision-analytic approach with particular consideration being given to different prior distributions and loss functions as well as the robustness of the proposed procedure. It also addresses the complications that arise in counting experiments that are conducted under real-world circumstances. The chapter further describes the use of Poisson distributed variables for forensic inference about the selected propositions of interest using graphical models. Finally, it presents the Bayesian procedure for learning about population means of Normal variables.

Controlled Vocabulary Terms

Bayes estimator; mean; point estimation; Poisson distribution