Data Analysis in Forensic Science : A Bayesian Decision Perspective

by Franco Taroni (Univ. of Lausanne, Switzerland); Silvia Bozza (Universit`a di Venezia); Alex Biedermann (Lausanne University, Switzerland ); Paolo Garbolino (IUAV University, Venice, Italy); Colin Aitken (Edinburgh University, UK)

• ISBN13: 9780470665077
• ISBN10: 0470665076
• Format: eBook
• Copyright: 2010-03-01
• Publisher: Wiley

Summary

This is the first text to examine the use of statistical methods in forensic science and bayesian statistics in combination.The book is split into two parts: Part One concentrates on the philosophies of statistical inference. Chapter One examines the differences between the frequentist, the likelihood and the Bayesian perspectives, before Chapter Two explores the Bayesian decision-theoretic perspective further, and looks at the benefits it carries.Part Two then introduces the reader to the practical aspects involved: the application, interpretation, summary and presentation of data analyses are all examined from a Bayesian decision-theoretic perspective. A wide range of statistical methods, essential in the analysis of forensic scientific data is explored. These include the comparison of allele proportions in populations, the comparison of means, the choice of sampling size, and the discrimination of items of evidence of unknown origin into predefined populations.Throughout this practical appraisal there are a wide variety of examples taken from the routine work of forensic scientists. These applications are demonstrated in the ever-more popular R language. The reader is taken through these applied examples in a step-by-step approach, discussing the methods at each stage.

Table of Contents

Foreword

Preface

The Foundations of Inference and Decision in Forensic Science

Introduction

The Inevitability of Uncertainty

Desiderata in Evidential Assessment

The Importance of the Propositional Framework and the Nature of Evidential Assessment

From Desiderata to Applications

The Bayesian Core of Forensic Science

Structure of the Book

Scientific Reasoning and Decision Making

Coherent Reasoning Under Uncertainty

Coherent Decision Making Under Uncertainty of Reasoning

Scientific Reasoning as Coherent Decision Making

Forensic Reasoning as Coherent Decision Making

Concepts of Statistical Science and Decision Theory

Random Variables and Distribution Functions

Statistical Inference and Decision Theory

The Bayesian Paradigm

Bayesian Decision Theory

R Code

Forensic Data Analysis

Point Estimation

Introduction

Bayesian Decision for a Proportion

Bayesian Decision for a Poisson Mean

Bayesian Decision for Normal Mean

R Code

Credible Intervals

Introduction

Credible Intervals

Decision-Theoretic Evaluation of Credible Intervals

R Code

Hypothesis Testing

Introduction

Bayesian Hypothesis Testing

One-sided testing

Two-Sided Testing

R Code

Sampling

Introduction

Sampling Inspection

Graphical Models for Sampling Inspection

Sampling Inspection under a Decision-Theoretic Approach

R Code

Classification of Observations

Introduction

Standards of Coherent Classification

Comparing Models using Discrete Data

Comparison of Models using Continuous Data

Non-Normal Distributions and Cocaine on Bank Notes

A note on Multivariate Continuous Data

R Code

Bayesian Forensic Data Analysis: Conclusions and Implications

Introduction

What is the Past and Current Position of Statistics in Forensic Science?

Why Should Forensic Scientists Conform to a Bayesian Framework for Inference and Decision Making?

Why Regard Probability as a Personal Degree of Belief?

Why Should Scientists be Aware of Decision Analysis?

How to Implement Bayesian Inference and Decision Analysis?

Discrete Distributions

Continuous Distributions

Bibliography

Author Index

Subject Index

Table of Contents provided by Publisher. All Rights Reserved.