rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Bayes Factors for Forensic Decision Analyses with R
  name:
    name part: Bozza, Silvia.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Taroni, Franco.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Biedermann, Alex.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2022
    edition: 1st ed. 2022.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XII, 187 pages 22 illustrations, 5 illustrations in color)
  abstract: Bayes Factors for Forensic Decision Analyses with R provides a self-contained introduction to computational Bayesian statistics using R. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability-keeping theoretical and philosophical justifications limited. It offers a balanced approach to three naturally interrelated topics: Probabilistic Inference - Relies on the core concept of Bayesian inferential statistics, to help practicing forensic scientists in the logical and balanced evaluation of the weight of evidence. Decision Making - Features how Bayes factors are interpreted in practical applications to help address questions of decision analysis involving the use of forensic science in the law. Operational Relevance - Combines inference and decision, backed up with practical examples and complete sample code in R, including sensitivity analyses and discussion on how to interpret results in context. Over the past decades, probabilistic methods have established a firm position as a reference approach for the management of uncertainty in virtually all areas of science, including forensic science, with Bayes' theorem providing the fundamental logical tenet for assessing how new information-scientific evidence-ought to be weighed. Central to this approach is the Bayes factor, which clarifies the evidential meaning of new information, by providing a measure of the change in the odds in favor of a proposition of interest, when going from the prior to the posterior distribution. Bayes factors should guide the scientist's thinking about the value of scientific evidence and form the basis of logical and balanced reporting practices, thus representing essential foundations for rational decision making under uncertainty. This book would be relevant to students, practitioners, and applied statisticians interested in inference and decision analyses in the critical field of forensic scienc…
  table of contents: Chapter 1: Introduction to the Bayes factor and decision analysis -- Chapter 2: Bayes factor for model choice -- Chapter 3: Bayes factor for evaluative purposes -- Chapter 4: Bayes factor for investigative purposes.
  note: by Silvia Bozza, Franco Taroni, Alex Biedermann.
  note: Open Access
  subject:
    topic: Statistics
  subject:
    topic: Mathematical statistics-Data processing
  subject:
    topic: Forensic sciences
  subject:
    topic: Medical jurisprudence
  subject:
    topic: Forensic psychology
  subject:
    topic: Social sciences-Statistical methods
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Statistics and Computing
  subject:
    topic: Forensic Science
  subject:
    topic: Forensic Medicine
  subject:
    topic: Forensic Psychology
  subject:
    topic: Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer Nature eBook
  related item:
    title info:
      title: Springer Texts in Statistics
  identifier: 9978-3-031-09839-0
  identifier: 9783031098390
  identifier: https://doi.org/10.1007/978-3-031-09839-0
  access condition: Open Access
  record info:
    record creation date: 221031
    record change date: 20230829013228.2
    record identifier: in00004658874