PDF Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

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Using Bayesian networks to guide the assessment of new evidence in an appeal case

Doi: A Bayesian approach to adult human age estimation from dental observations by Johanson's age changes. J Forensic Sci ; Non-adult dental age assessment: Correspondence analysis and linear regression versus Bayesian predictions. Probabilistic classification of age by third molar development: The use of soft evidence.

Optimal age classification of young individuals based on dental evidence in civil and criminal proceedings.

Using Bayesian networks to guide the assessment of new evidence in an appeal case

Human dental age estimation using third molar developmental stages: Does a Bayesian approach outperform regression models to discriminate between juveniles and adults? Taroni F, Biedermann A. Bayesian networks. Chichester: John Wiley and Sons; The subjectivist interpretation of probability and the problem of individualisation in forensic science.

Sci Justice ; Nonparametric calibration for age estimation.


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Appl Stat ; Bone age determination based on the study of the medial extremity of the clavicle. Eur Radiol ; Epiphyseal union of the anterior iliac crest and medial clavicle in a modern multiracial sample of American males and females.

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Am J Phys Anthropol ; Practical imaging techniques for age evaluation. Forensic age estimation in unaccompanied minors and young living adults. In: Vieira DN, editor. Rijeka: InTech; Fundamentals of probability and statistical evidence. In: Royal Statistical Society. Royal Statistical Society; Statistical interpretation of evidence: Bayesian analysis.


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Implementing statistical learning methods through Bayesian networks. Part 1: A guide to Bayesian parameter estimation using forensic science data. Howson C. Bayesianism in statistics. In: Swinburne R, editor. Bayes's Theorem. Oxford: Oxford University Press; Transition analysis: A new method for estimating age from skeletons. Age Distributions from Skeletal Samples. Cambridge: Cambridge University Press; Markov chain Monte Carlo estimation of hazard model parameters in paleodemographyr. Statistical basis for positive identification in forensic anthropology.

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Perception vs. Inference

Description "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, UK Continuing 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.

Bayesian Networks

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.

General intelligence

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.

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Read Bayesian Networks For Probabilistic Inference And Decision Analysis In Forensic Science

Toon meer Toon minder. Recensie s International Statistical Institute, Oct Overige kenmerken Extra groot lettertype Nee. Reviews Schrijf een review. Bindwijze: Hardcover. Niet leverbaar. Wil je eenmalig een e-mail ontvangen zodra het weer leverbaar is? Breng me op de hoogte Op verlanglijstje. Houd er rekening mee dat het artikel niet altijd weer terug op voorraad komt.