William J. Bodziak, ,Tire Tread and Tire Track Evidence Recovery and Forensic Examination (2008) CRC Press ISBN 13:978-0-8493-7247-6.
✍ Scribed by Lesley Hammer
- Book ID
- 104092133
- Publisher
- Elsevier Science
- Year
- 2009
- Tongue
- English
- Weight
- 46 KB
- Volume
- 49
- Category
- Article
- ISSN
- 1355-0306
No coin nor oath required. For personal study only.
✦ Synopsis
Book reviews (among others) demonstrating the wide applicability of the technique. The introduction provides a comprehensive description of the benefits, as well as the drawbacks, of probabilistic modelling. Bayesian Networks are introduced in detail and examples are given. A previous statistical background is needed to fully understand and appreciate the introduction as well as the rest of the text.
As a forensic scientist the chapters of interest, besides the introduction and conclusion, are crime risk factor analysis (Chapter 5), inference problems in forensic science (Chapter 7) and, potentially, terrorism risk management (Chapter 14).
Chapter 5 describes a crime risk factor analysis pilot scheme as an example of using a Bayesian Network to predict crime in Bangkok. In the end some obvious correlations are found, i.e. there are an increased number of murder cases in areas with high drug sales. Chapter 7 describes Bayesian Networks as they are applied to forensic evidence in 13 pages. This chapter describes how Bayesian Networks can be applied to different types of evidence, from DNA to shoemarks. As expected in 13 pages, this chapter can only brush the surface of the use of the use of Bayesian Networks in forensic science. Two worked examples are given, but due to the limited space the reader is referred out of text for a full explanation. Chapter 14 details the way the US is using Bayesian Networks to assess potential terrorist threats against military and civilian sites. It gives a detailed framework of how the level of a terrorist threat is determined and which could be adapted for other countries.
Overall this text accomplishes what it claims in demonstrating the uses of Bayesian networks to a wide range of data types. It is beneficial for those who are already familiar with, but who wish to learn the wider applicability of, Bayesian networks. This text is not suitable as an introductory text unless it is used in conjunction with other basic statistical texts. Forensic scientists wishing to use Bayesian Networks may opt for a more specialised text such as Bayesian Networks and Probabilistic Inference in Forensic Science (Wiley) which may be more suitable and offer more, relevant, worked examples.