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Titlebook: Bayes Factors for Forensic Decision Analyses with R; Silvia Bozza,Franco Taroni,Alex Biedermann Textbook‘‘‘‘‘‘‘‘ 2022 Springer Nature Swit

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期刊全称Bayes Factors for Forensic Decision Analyses with R
影响因子2023Silvia Bozza,Franco Taroni,Alex Biedermann
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发行地址Emphasizes the role of Bayes factor guided reasoning as a necessary preliminary to coherent decision analysis.Presents computational details and interpretation of output, recommended in forensic scien
学科分类Springer Texts in Statistics
图书封面Titlebook: Bayes Factors for Forensic Decision Analyses with R;  Silvia Bozza,Franco Taroni,Alex Biedermann Textbook‘‘‘‘‘‘‘‘ 2022 Springer Nature Swit
影响因子.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 sci
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https://doi.org/10.1007/978-3-031-09839-0Bayes factor; scientific evidence; decision making; forensic science; uncertainty management; probability
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Celeste Lyn Paul,Kirsten Whitleylogical inference and decision. The chapter introduces the reader to three key topics that forensic scientists commonly encounter and that are treated in this book: model choice, evaluation and investigation. For each of these themes, Bayes factors will be developed in later chapters and discussed u
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https://doi.org/10.1007/978-3-642-39345-7inuous multivariate data. The latter may present a complex dependence structure that will be handled by means of multilevel models. The notion of “evaluative purpose” is understood here as referring to situations in which material of known source (control material) and evidential material of unknown
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