Suchergebnisse

The logic of uncertainty -- The logic of Bayesian networks -- Evaluation of scientific evidence -- Bayesian networks for evaluating scientific evidence -- DNA evidence -- Transfer evidence -- Aspects of the combination of evidence -- Pre-assessment -- Qualitative and sensitivity analyses -- Continuous networks -- Further applications

The logic of uncertainty -- The logic of Bayesian networks -- Evaluation of scientific evidence -- Bayesian networks for evaluating scientific evidence -- DNA evidence -- Transfer evidence -- Aspects of the combination of evidence -- Pre-assessment -- Qualitative and sensitivity analyses -- Continuous networks -- Further applications.

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 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.

"With the increase in the availability of data, the role of statistical and probabilistic reasoning is becoming ever more high profile, and courts are increasingly aware of the importance of the proper assessment of evidence in which there is random variation. Statistical techniques allow the forensic scientist to evaluate and interpret evidence where there is an element of uncertainty. Since its first publication in 1995, this highly regarded book has been considered as the leading text in statistical evaluation of forensic evidence. Bringing together authors from the fields of both statistics and forensic science, all international experts in the evaluation and interpretation of evidence, the third edition will be fully revised and updated to reflect the latest research and developments in this field."--

A broad range of questions at various instances in the legal process can be stated and analysed in terms of formal decision theoretic models, with results conveyed in graphical terms, such as decision trees. However, the real-world decision problems encountered by the participants of a legal process, including judges, prosecutors and attorneys, present challenging features, such as multiple competing propositions, variable costs and uncertain process outcomes. This complicates decision theoretic computations and the use of diagrammatic devices such as decision trees which mainly provide static views of selected features of a given problem. Yet, the issues are inherently dynamic, and the complexity of strategic planning and assessing legal tactics-given a party's standpoint-increases even further when considerations are extended to information provided by forensic science services. This is because introducing results of forensic examinations may impact on the probability of various trial outcomes and hence crucially impact on a party's interests. In this paper, we analyse and discuss examples of decision problems at the interface of the law and forensic science using influence diagrams (i.e., Bayesian decision networks). Such models, hereafter called normative decision support structures, can be operationally implemented through commercially and academically available software systems. These normative decision support structures represent core computational models that can be integrated as part of decision and litigation support systems, to help the participants of a legal process answer a variety of questions regarding complex strategic decisions.

"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

Importance: Juvenile amyotrophic lateral sclerosis (ALS) is a rare form of ALS characterized by age of symptom onset less than 25 years and a variable presentation. Objective: To identify the genetic variants associated with juvenile ALS. Design, Setting, and Participants: In this multicenter family-based genetic study, trio whole-exome sequencing was performed to identify the disease-associated gene in a case series of unrelated patients diagnosed with juvenile ALS and severe growth retardation. The patients and their family members were enrolled at academic hospitals and a government research facility between March 1, 2016, and March 13, 2020, and were observed until October 1, 2020. Whole-exome sequencing was also performed in a series of patients with juvenile ALS. A total of 66 patients with juvenile ALS and 6258 adult patients with ALS participated in the study. Patients were selected for the study based on their diagnosis, and all eligible participants were enrolled in the study. None of the participants had a family history of neurological disorders, suggesting de novo variants as the underlying genetic mechanism. Main Outcomes and Measures: De novo variants present only in the index case and not in unaffected family members. Results: Trio whole-exome sequencing was performed in 3 patients diagnosed with juvenile ALS and their parents. An additional 63 patients with juvenile ALS and 6258 adult patients with ALS were subsequently screened for variants in the SPTLC1 gene. De novo variants in SPTLC1 (p.Ala20Ser in 2 patients and p.Ser331Tyr in 1 patient) were identified in 3 unrelated patients diagnosed with juvenile ALS and failure to thrive. A fourth variant (p.Leu39del) was identified in a patient with juvenile ALS where parental DNA was unavailable. Variants in this gene have been previously shown to be associated with autosomal-dominant hereditary sensory autonomic neuropathy, type 1A, by disrupting an essential enzyme complex in the sphingolipid synthesis pathway. Conclusions and Relevance: These data broaden the phenotype associated with SPTLC1 and suggest that patients presenting with juvenile ALS should be screened for variants in this gene.