Description |
1 online resource (XII, 187 p. 22 illus., 5 illus. in color.) |
Series |
Springer Texts in Statistics, 2197-4136 |
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Springer texts in statistics, 2197-4136
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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 |
Summary |
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 science. It could be used to support practical courses on Bayesian statistics and decision theory at both undergraduate and graduate levels, and will be of equal interest to forensic scientists and practitioners of Bayesian statistics for driving their evaluations and the use of R for their purposes. This book is Open Access |
Analysis |
Bayes factor |
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scientific evidence |
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decision making |
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forensic science |
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uncertainty management |
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probability theory |
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forensic |
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decision analysis |
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Bayesian modeling |
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R |
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Bayesian statistics |
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probabilistic inference |
Bibliography |
Includes bibliographical references and index |
Notes |
English |
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Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung |
In |
OAPEN (Open Access Publishing in European Networks) OAPEN |
Subject |
Statistics.
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Mathematical statistics--Data processing
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Forensic sciences.
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Medical jurisprudence.
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Forensic psychology.
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Social sciences--Statistical methods
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statistics.
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forensic science.
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forensic medicine.
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Estadística
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Estadística matemática -- Datos-Tratamiento
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Criminalística
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Medicina legal
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Psicología legal
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Ciencias sociales -- Métodos estadísticos
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Forensic psychology
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Forensic sciences
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Mathematical statistics -- Data processing
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Medical jurisprudence
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Social sciences -- Statistical methods
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Statistics
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Estadística bayesiana.
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Processament de dades.
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Criminalística.
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R (Llenguatge de programació)
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Genre/Form |
Llibres electrònics.
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Form |
Electronic book
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Author |
Taroni, Franco. author.
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Biedermann, Alex. author.
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ISBN |
3031098390 |
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9783031098390 |
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