Data Analysis in Forensic Science
A Bayesian Decision Perspective
Published on 7. March 2010
388 pages
978-0-470-66507-7 (ISBN)
Description
This is the first text to examine the use of statistical methods inforensic science and bayesian statistics in combination.
The book is split into two parts: Part One concentrates on thephilosophies of statistical inference. Chapter One examines thedifferences between the frequentist, the likelihood and theBayesian perspectives, before Chapter Two explores the Bayesiandecision-theoretic perspective further, and looks at the benefitsit carries.
Part Two then introduces the reader to the practical aspectsinvolved: the application, interpretation, summary and presentationof data analyses are all examined from a Bayesiandecision-theoretic perspective. A wide range of statisticalmethods, essential in the analysis of forensic scientific data isexplored. These include the comparison of allele proportions inpopulations, the comparison of means, the choice of sampling size,and the discrimination of items of evidence of unknown origin intopredefined populations.
Throughout this practical appraisal there are a wide variety ofexamples taken from the routine work of forensic scientists. Theseapplications are demonstrated in the ever-more popular R language.The reader is taken through these applied examples in astep-by-step approach, discussing the methods at each stage.
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Franco Taroni | Silvia Bozza | Alex Biedermann
Data Analysis in Forensic Science - A Bayesian Decision Perspective
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04/2010
Wiley
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Franco Taroni | Silvia Bozza | Alex Biedermann
Data Analysis in Forensic Science
A Bayesian Decision Perspective
Book
04/2010
Wiley
€95.50
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Persons
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.
Content
Foreword.
Preface.
I The Foundations of Inference and Decision in ForensicScience.
1 Introduction.
1.1 The Inevitability of Uncertainty.
1.2 Desiderata in Evidential Assessment.
1.3 The Importance of the Propositional Framework and the Natureof Evidential Assessment.
1.4 From Desiderata to Applications.
1.5 The Bayesian Core of Forensic Science.
1.6 Structure of the Book.
2 Scientific Reasoning and Decision Making.
2.1 Coherent Reasoning Under Uncertainty.
2.2 Coherent Decision Making Under Uncertainty of Reasoning.
2.3 Scientific Reasoning as Coherent Decision Making.
2.4 Forensic Reasoning as Coherent Decision Making.
3 Concepts of Statistical Science and DecisionTheory.
3.1 Random Variables and Distribution Functions.
3.2 Statistical Inference and Decision Theory.
3.3 The Bayesian Paradigm.
3.4 Bayesian Decision Theory.
3.5 R Code.
II Forensic Data Analysis.
4 Point Estimation.
4.1 Introduction.
4.2 Bayesian Decision for a Proportion.
4.3 Bayesian Decision for a Poisson Mean.
4.4 Bayesian Decision for Normal Mean.
4.5 R Code.
5 Credible Intervals.
5.1 Introduction.
5.2 Credible Intervals.
5.3 Decision-Theoretic Evaluation of Credible Intervals.
5.4 R Code.
6 Hypothesis Testing.
6.1 Introduction.
6.2 Bayesian Hypothesis Testing.
6.3 One-sided testing.
6.4 Two-Sided Testing.
6.5 R Code.
7 Sampling.
7.1 Introduction.
7.2 Sampling Inspection.
7.3 Graphical Models for Sampling Inspection.
7.4 Sampling Inspection under a Decision-Theoretic Approach.
7.5 R Code.
8 Classification of Observations.
8.1 Introduction.
8.2 Standards of Coherent Classification.
8.3 Comparing Models using Discrete Data.
8.4 Comparison of Models using Continuous Data.
8.5 Non-Normal Distributions and Cocaine on Bank Notes.
8.6 A note on Multivariate Continuous Data.
8.7 R Code.
9 Bayesian Forensic Data Analysis: Conclusions andImplications.
9.1 Introduction.
9.2 What is the Past and Current Position of Statistics inForensic Science?
9.3 Why Should Forensic Scientists Conform to a BayesianFramework for Inference and Decision Making?
9.4 Why Regard Probability as a Personal Degree of Belief?
9.5 Why Should Scientists be Aware of Decision Analysis?
9.6 How to Implement Bayesian Inference and DecisionAnalysis?
A Discrete Distributions.
B Continuous Distributions.
Bibliography.
Author Index.
Subject Index.
