| 期刊全稱(chēng) | Bayes Factors for Forensic Decision Analyses with R | | 影響因子2023 | Silvia Bozza,Franco Taroni,Alex Biedermann | | 視頻video | http://file.papertrans.cn/182/181816/181816.mp4 | | 發(fā)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 | | 學(xué)科分類(lèi) | Springer Texts in Statistics | | 圖書(shū)封面 |  | | 影響因子 | .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 | | Pindex | Textbook‘‘‘‘‘‘‘‘ 2022 |
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