Statistics and the Evaluation of Evidence for Forensic Scientists

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Aitken, Colin
Otros Autores: Taroni, Franco, Bozza, Silvia
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2020.
Edición:3rd ed.
Colección:Statistics in Practice Ser.
Temas:
Forensic sciences > Statistical methods.
Criminalistique > Méthodes statistiques.
Forensic sciences > Statistical methods
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Foreword
  • Preface to Third Edition
  • Preface to Second Edition
  • Preface to First Edition
  • Chapter 1 Uncertainty in Forensic Science
  • 1.1 Introduction
  • 1.2 Statistics and the Law
  • 1.3 Uncertainty in Scientific Evidence
  • 1.3.1 The Frequentist Method
  • 1.3.2 Stains of Body Fluids
  • 1.3.3 Glass Fragments
  • 1.4 Terminology
  • 1.5 Types of Data
  • 1.6 Populations
  • 1.7 Probability
  • 1.7.1 Introduction
  • 1.7.2 A Standard for Uncertainty
  • 1.7.3 Events
  • 1.7.4 Classical and Frequentist Definitions of Probability and Their Limitations
  • 1.7.5 Subjective Definition of Probability
  • 1.7.6 The Quantification of Probability Through a Betting Scheme
  • 1.7.7 Probabilities and Frequencies: The Role of Exchangeability
  • 1.7.8 Laws of Probability
  • 1.7.9 Dependent Events and Background Information
  • 1.7.10 Law of Total Probability
  • 1.7.11 Updating of Probabilities
  • Chapter 2 The Evaluation of Evidence
  • 2.1 Odds
  • 2.1.1 Complementary Events
  • 2.1.2 Examples
  • 2.1.3 Definition of Odds
  • 2.2 Bayes' Theorem
  • 2.2.1 Statement of the Theorem
  • 2.2.2 Examples
  • 2.3 The Odds Form of Bayes' Theorem
  • 2.3.1 Likelihood Ratio
  • 2.3.2 Bayes' Factor and Likelihood Ratio
  • 2.3.3 Three-Way Tables
  • 2.3.4 Logarithm of the Likelihood Ratio
  • 2.4 The Value of Evidence
  • 2.4.1 Evaluation of Forensic Evidence
  • 2.4.2 Justification of the Use of the Likelihood Ratio
  • 2.4.3 Single Value for the Likelihood Ratio
  • 2.4.4 Role of Background Information
  • 2.4.5 Summary of Competing Propositions
  • 2.4.6 Qualitative Scale for the Value of the Evidence
  • 2.5 Errors in Interpretation
  • 2.5.1 Fallacy of the Transposed Conditional
  • 2.5.2 Source Probability Error
  • 2.5.3 Ultimate Issue Error
  • 2.5.4 Defence Attorney's Fallacy
  • 2.5.5 Probability (Another Match) Error
  • 2.5.6 Numerical Conversion Error
  • 2.5.7 False Positive Fallacy
  • 2.5.8 Expected Value Fallacy
  • 2.5.9 Uniqueness
  • 2.5.10 Other Difficulties
  • 2.5.11 Empirical Evidence of Errors in Interpretation
  • 2.6 Misinterpretations
  • 2.7 Explanation of Transposed Conditional, Defence Attorney's and False Positive Fallacies
  • 2.7.1 Explanation of the Fallacy of the Transposed Conditional
  • 2.7.2 Explanation of the Defence Attorney's Fallacy
  • 2.7.3 Explanation of the False Positive Fallacy
  • 2.8 Making Coherent Decisions
  • 2.8.1 Elements of Statistical Decision Theory
  • 2.8.2 Decision Analysis: An Example
  • 2.9 Graphical Probabilistic Models: Bayesian Networks
  • 2.9.1 Elements of the Bayesian Networks
  • 2.9.2 The Construction of Bayesian Networks
  • 2.9.3 Bayesian Decision Networks (Influence Diagrams)
  • Chapter 3 Historical Review
  • 3.1 Early History
  • 3.2 The Dreyfus Case
  • 3.3 Statistical Arguments by Early Twentieth-Century Forensic Scientists
  • 3.4 People v. Collins
  • 3.5 Discriminating Power
  • 3.5.1 Derivation

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