Bayesian networks for probabilistic inference and decision analysis in forensic science:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Chichester, West Sussex
Wiley
2014
|
| Ausgabe: | Second edition |
| Schriftenreihe: | Statistics in practice
|
| Schlagworte: | |
| Online-Zugang: | FRO01 UBG01 Volltext |
| Beschreibung: | Includes bibliographical references and index "This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation"Dr. Ian Evett, Principal Forensic Services Ltd, London, UKContinuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system.Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks.*Includes self-contained introductions to probability and decision theory.*Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models.*Features implementation of the methodology with reference to commercial and academically available software.*Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases.*Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning.*Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them.*Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background.*Includes a foreword by Ian Evett.The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information |
| Beschreibung: | 1 Online-Ressource (xxiv, 443 pages) |
| ISBN: | 9781118914755 1118914759 9781118914762 1118914767 1118914740 9781118914748 |
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| 500 | |a "This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation"Dr. Ian Evett, Principal Forensic Services Ltd, London, UKContinuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. | ||
| 500 | |a Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system.Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks.*Includes self-contained introductions to probability and decision theory.*Develops the characteristics of Bayesian networks, | ||
| 500 | |a object-oriented Bayesian networks and their extension to decision models.*Features implementation of the methodology with reference to commercial and academically available software.*Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases.*Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning.*Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them.*Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background.*Includes a foreword by Ian Evett.The clear and accessible style of this second edition makes this book ideal for all forensic scientists, | ||
| 500 | |a applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information | ||
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| format | Electronic eBook |
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| id | DE-604.BV043396959 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:24:52Z |
| institution | BVB |
| isbn | 9781118914755 1118914759 9781118914762 1118914767 1118914740 9781118914748 |
| language | English |
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| series2 | Statistics in practice |
| spelling | Taroni, Franco Verfasser aut Bayesian networks for probabilistic inference and decision analysis in forensic science Franco Taroni, Alex Biedermann, Silvia Bozza, Paolo Garbolino, Colin Aitken Second edition Chichester, West Sussex Wiley 2014 1 Online-Ressource (xxiv, 443 pages) txt rdacontent c rdamedia cr rdacarrier Statistics in practice Includes bibliographical references and index "This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation"Dr. Ian Evett, Principal Forensic Services Ltd, London, UKContinuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system.Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks.*Includes self-contained introductions to probability and decision theory.*Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models.*Features implementation of the methodology with reference to commercial and academically available software.*Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases.*Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning.*Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them.*Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background.*Includes a foreword by Ian Evett.The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information BUSINESS & ECONOMICS / Infrastructure bisacsh SOCIAL SCIENCE / General bisacsh Probabiliteit gtt Statistische analyse gtt Methode van Bayes gtt Bewijs (recht) gtt Criminalistiek gtt Bayesian statistical decision theory / Graphic methods local Uncertainty (Information theory) / Graphic methods local Forensic sciences / Graphic methods local Wirtschaft Bayesian statistical decision theory / Graphic methods Uncertainty (Information theory) / Graphic methods Forensic sciences / Graphic methods Electronic books Biedermann, Alex Sonstige oth Bozza, Silvia Sonstige oth Garbolino, Paolo Sonstige oth Aitken, C. G. G. Sonstige oth Erscheint auch als Druck-Ausgabe, Hardcover 0-470-97973-9 Erscheint auch als Druck-Ausgabe, Hardcover 978-0-470-97973-0 https://onlinelibrary.wiley.com/doi/book/10.1002/9781118914762 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Taroni, Franco Bayesian networks for probabilistic inference and decision analysis in forensic science BUSINESS & ECONOMICS / Infrastructure bisacsh SOCIAL SCIENCE / General bisacsh Probabiliteit gtt Statistische analyse gtt Methode van Bayes gtt Bewijs (recht) gtt Criminalistiek gtt Bayesian statistical decision theory / Graphic methods local Uncertainty (Information theory) / Graphic methods local Forensic sciences / Graphic methods local Wirtschaft Bayesian statistical decision theory / Graphic methods Uncertainty (Information theory) / Graphic methods Forensic sciences / Graphic methods |
| title | Bayesian networks for probabilistic inference and decision analysis in forensic science |
| title_auth | Bayesian networks for probabilistic inference and decision analysis in forensic science |
| title_exact_search | Bayesian networks for probabilistic inference and decision analysis in forensic science |
| title_full | Bayesian networks for probabilistic inference and decision analysis in forensic science Franco Taroni, Alex Biedermann, Silvia Bozza, Paolo Garbolino, Colin Aitken |
| title_fullStr | Bayesian networks for probabilistic inference and decision analysis in forensic science Franco Taroni, Alex Biedermann, Silvia Bozza, Paolo Garbolino, Colin Aitken |
| title_full_unstemmed | Bayesian networks for probabilistic inference and decision analysis in forensic science Franco Taroni, Alex Biedermann, Silvia Bozza, Paolo Garbolino, Colin Aitken |
| title_short | Bayesian networks for probabilistic inference and decision analysis in forensic science |
| title_sort | bayesian networks for probabilistic inference and decision analysis in forensic science |
| topic | BUSINESS & ECONOMICS / Infrastructure bisacsh SOCIAL SCIENCE / General bisacsh Probabiliteit gtt Statistische analyse gtt Methode van Bayes gtt Bewijs (recht) gtt Criminalistiek gtt Bayesian statistical decision theory / Graphic methods local Uncertainty (Information theory) / Graphic methods local Forensic sciences / Graphic methods local Wirtschaft Bayesian statistical decision theory / Graphic methods Uncertainty (Information theory) / Graphic methods Forensic sciences / Graphic methods |
| topic_facet | BUSINESS & ECONOMICS / Infrastructure SOCIAL SCIENCE / General Probabiliteit Statistische analyse Methode van Bayes Bewijs (recht) Criminalistiek Bayesian statistical decision theory / Graphic methods Uncertainty (Information theory) / Graphic methods Forensic sciences / Graphic methods Wirtschaft |
| url | https://onlinelibrary.wiley.com/doi/book/10.1002/9781118914762 |
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