7. Sampling

  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.ch7

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) Sampling, in Data Analysis in Forensic Science: A Bayesian Decision Perspective, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470665084.ch7

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



  • Bayesian inference;
  • decision-theoretic approach;
  • graphical models;
  • sampling inspection;
  • sequential analysis


Although the handling of uncertainty through probability is an essential aspect of sampling scenarios, the situation actually faced by a customer of forensic expertise is one that contains elements that allow the outcome to be considered as a problem of decision making. This chapter focuses on a sequential way of proceeding, in which the size of a sample is not predetermined. The proposed procedure involves—after each observation of a sampled item—a decision about whether it is advisable to draw a conclusion about the value of ? or whether more observations should be gathered. A single sampling plan and sampling based on a sequential analysis are among the most common methods for taking observations. The chapter outlines Bayesian approaches using beta and beta-binomial distributions that are implemented in computerized formats.

Controlled Vocabulary Terms

Bayesian inference; sequential analysis; sequential sampling