000			04998nam a22006495i 4500
001			978-3-031-09839-0
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005			20240508090327.0
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008			221031s2022 sz | s |||| 0|eng d
020			_a9783031098390 _9978-3-031-09839-0
024	7		_a10.1007/978-3-031-09839-0 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
072		7	_aPBT _2thema
082	0	4	_a519.5 _223
100	1		_aBozza, Silvia. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aBayes Factors for Forensic Decision Analyses with R _h[electronic resource] / _cby Silvia Bozza, Franco Taroni, Alex Biedermann.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_aXII, 187 p. 22 illus., 5 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Texts in Statistics, _x2197-4136
505	0		_aChapter 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.
506	0		_aOpen Access
520			_aBayes 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.
650		0	_aStatistics .
650		0	_aMathematical statistics _xData processing.
650		0	_aForensic sciences.
650		0	_aMedical jurisprudence.
650		0	_aForensic psychology.
650		0	_aSocial sciences _xStatistical methods.
650	1	4	_aStatistical Theory and Methods.
650	2	4	_aStatistics and Computing.
650	2	4	_aForensic Science.
650	2	4	_aForensic Medicine.
650	2	4	_aForensic Psychology.
650	2	4	_aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.
700	1		_aTaroni, Franco. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aBiedermann, Alex. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031098383
776	0	8	_iPrinted edition: _z9783031098406
776	0	8	_iPrinted edition: _z9783031098413
830		0	_aSpringer Texts in Statistics, _x2197-4136
856	4	0	_uhttps://doi.org/10.1007/978-3-031-09839-0
912			_aZDB-2-SMA
912			_aZDB-2-SXMS
912			_aZDB-2-SOB
999			_c37767 _d37767