Data Analysis in Forensic Science: A Bayesian Decision Perspective (Statistics in Practice) - Hardcover

Über die Autorin bzw. den Autor

Franco Taroni, Institute de Police Scientifique et de Criminologie, University of Lausanne, Switzerland.

Silvia Bozza, Departimento di Statistica, Universit`a di Venezia, Italy.

Alex Biedermann, Bundeskriminalpolizei, Kommissariat Kriminaltechnik, Switzerland.

Paolo Garbolino, Faculty of Arts and Design, IUAV University, Venice, Italy.

Colin Aitken, School of Mathematics, University of Edinburgh, UK.

Von der hinteren Coverseite

The use of formal statistical methods to analyse quantitative data in forensic science has increased considerably over the last few years. Students, researchers and practitioners in forensic science regularly ask questions concerning the relative merits of differing approaches, in particular the frequentist and Bayesian approaches, to statistical inference in the forensic context. The ideas of the Bayesian approach in forensic science are now being extended to include decision theory and the associated concept of utility.

Data Analysis in Forensic Science: A Bayesian Decision Perspective sets forth procedures for data analysis that rely on the decision-theoretic approach to inference. Emphasis is made on foundational philosophical tenets as well as the implications of the decision-theoretic approach in practice. This book discusses a range of statistical decision-theoretic methods that are useful in the analysis of forensic scientific data. Forensic scientific examples include point estimation, the comparison of means and proportions in populations, the choice of sample size and the classification of items of evidence of unknown origin into predefined populations.

Key Features:

• Comprehensive coverage of the analysis of forensic data from a Bayesian perspective, featuring numerous real-world examples and applications.
• Explanation and definition of key concepts and methods from historical, philosophical and theoretical points of view.
• An incremental approach for consideration of examples inspired and motivated by issues that may arise in routine forensic practice.
• Consideration of the arguments and methods, including those of decision theory, used at each stage of the analyses.
• Inclusion of code written in R to offer an opportunity for enhanced exploration of the ideas.
• The use of graphical models (e.g. Bayesian networks) to illustrate selected applications of Bayesian methodology.

Aus dem Klappentext

The use of formal statistical methods to analyse quantitative data in forensic science has increased considerably over the last few years. Students, researchers and practitioners in forensic science regularly ask questions concerning the relative merits of differing approaches, in particular the frequentist and Bayesian approaches, to statistical inference in the forensic context. The ideas of the Bayesian approach in forensic science are now being extended to include decision theory and the associated concept of utility.

Data Analysis in Forensic Science: A Bayesian Decision Perspective sets forth procedures for data analysis that rely on the decision-theoretic approach to inference. Emphasis is made on foundational philosophical tenets as well as the implications of the decision-theoretic approach in practice. This book discusses a range of statistical decision-theoretic methods that are useful in the analysis of forensic scientific data. Forensic scientific examples include point estimation, the comparison of means and proportions in populations, the choice of sample size and the classification of items of evidence of unknown origin into predefined populations.

Key Features:

• Comprehensive coverage of the analysis of forensic data from a Bayesian perspective, featuring numerous real-world examples and applications.
• Explanation and definition of key concepts and methods from historical, philosophical and theoretical points of view.
• An incremental approach for consideration of examples inspired and motivated by issues that may arise in routine forensic practice.
• Consideration of the arguments and methods, including those of decision theory, used at each stage of the analyses.
• Inclusion of code written in R to offer an opportunity for enhanced exploration of the ideas.
• The use of graphical models (e.g. Bayesian networks) to illustrate selected applications of Bayesian methodology.