Bayes Factors for Forensic Decision Analyses with R

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

Bayes Factors for Forensic Decision Analyses with R

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

Modeling the forensic two-trace problem with Bayesian networks

The forensic two-trace problem is a perplexing inference problem introduced by Evett (J Forensic Sci Soc 27:375-381, 1987). Different possible ways of wording the competing pair of propositions (i.e., one proposition advanced by the prosecution and one proposition advanced by the defence) led to different quantifications of the value of the evidence (Meester and Sjerps in Biometrics 59:727-732, 2003). Here, we re-examine this scenario with the aim of clarifying the interrelationships that exist between the different solutions, and in this way, produce a global vision of the problem. We propose to investigate the different expressions for evaluating the value of the evidence by using a graphical approach, i.e. Bayesian networks, to model the rationale behind each of the proposed solutions and the assumptions made on the unknown parameters in this proble

L'individualizzazione come decisione

This paper is an Italian translation of the article Biedermann A., Bozza S., Taroni F. 2016, The decisionalization of individualization, Forensic Science International, 266, 29-38, doi: http://dx.doi.org/10.1016/j.forsciint.2016.04.029, with a foreword by Marcello di Bello (Department of Philosophy, Lehman College, City University of New York)

A formal approach to qualifying and quantifying the ‘goodness’ of forensic identification decisions

In this article, we review and analyse common understandings of the degree to which forensic inference of source—also called identification or individualization—can be approached with statistics and is referred to, increasingly often, as a decision. We also consider this topic from the strongly empirical perspective of PCAST (2016) in its recent review of forensic science practice. We will point out why and how these views of forensic identification as a decision, and empirical approaches to it (namely experiments by multiple experts under controlled conditions), provide only descriptive measures of expert performance and of general scientific validity regarding particular forensic branches (e.g. fin- germark examination). Although relevant to help assess whether the identification practice of a given forensic field can be trusted, these empirical accounts do not address the separate question of what ought to be a sensible, or ‘good’ in some sense, (identification-)decision to make in a particular case. The latter question, as we will argue, requires additional considerations, such as decision-making goals. We will point out that a formal approach to qualifying and quantifying the relative merit of competing forensic decisions can be considered within an extended view of statistics in which data analysis and inference are a necessary but not sufficient preliminary

Dynamic signatures: A review of dynamic feature variation and forensic methodology

This article focuses on dynamic signatures and their features. It provides a detailed and critical review of dynamic feature variations and circumstantial parameters affecting dynamic signatures. The state of the art summarizes available knowledge, meant to assist the forensic practitioner in cases presenting extraordinary writing conditions. The studied parameters include hardware-related issues, aging and the influence of time, as well as physical and mental states of the writer. Some parameters, such as drug and alcohol abuse or medication, have very strong effects on handwriting and signature dynamics. Other conditions such as the writer’s posture and fatigue have been found to affect feature variation less severely. The need for further research about the influence of these parameters, as well as handwriting dynamics in general is highlighted. These factors are relevant to the examiner in the assessment of the probative value of the reported features. Additionally, methodology for forensic examination of dynamic signatures is discussed. Available methodology and procedures are reviewed, while pointing out major technical and methodological advances in the field of forensic handwriting examination. The need for sharing the best practice manuals, standard operating procedures and methodologies to favor further progress is accentuated

Normal and pathogenic variation of RFC1 repeat expansions: implications for clinical diagnosis

Cerebellar Ataxia, Neuropathy and Vestibular Areflexia Syndrome (CANVAS) is an autosomal recessive neurodegenerative disease, usually caused by biallelic AAGGG repeat expansions in RFC1. In this study, we leveraged whole genome sequencing (WGS) data from nearly 10,000 individuals recruited within the Genomics England sequencing project to investigate the normal and pathogenic variation of the RFC1 repeat. We identified three novel repeat motifs, AGGGC (n=6 from 5 families), AAGGC (n=2 from 1 family), AGAGG (n=1), associated with CANVAS in the homozygous or compound heterozygous state with the common pathogenic AAGGG expansion. While AAAAG, AAAGGG and AAGAG expansions appear to be benign, here we show a pathogenic role for large AAAGG repeat configuration expansions (n=5). Long read sequencing was used to fully characterise the entire repeat sequence and revealed a pure AGGGC expansion in six patients, whereas the other patients presented complex motifs with AAGGG or AAAGG interruptions. All pathogenic motifs seem to have arisen from a common haplotype and are predicted to form highly stable G quadruplexes, which have been previously demonstrated to affect gene transcription in other conditions. The assessment of these novel configurations is warranted in CANVAS patients with negative or inconclusive genetic testing. Particular attention should be paid to carriers of compound AAGGG/AAAGG expansions, since the AAAGG motif when very large (>500 repeats) or in the presence of AAGGG interruptions. Accurate sizing and full sequencing of the satellite repeat with long read is recommended in clinically selected cases, in order to achieve an accurate molecular diagnosis and counsel patients and their families

Primary brain calcification: an international study reporting novel variants and associated phenotypes.

Primary familial brain calcification (PFBC) is a rare cerebral microvascular calcifying disorder with a wide spectrum of motor, cognitive, and neuropsychiatric symptoms. It is typically inherited as an autosomal-dominant trait with four causative genes identified so far: SLC20A2, PDGFRB, PDGFB, and XPR1. Our study aimed at screening the coding regions of these genes in a series of 177 unrelated probands that fulfilled the diagnostic criteria for primary brain calcification regardless of their family history. Sequence variants were classified as pathogenic, likely pathogenic, or of uncertain significance (VUS), based on the ACMG-AMP recommendations. We identified 45 probands (25.4%) carrying either pathogenic or likely pathogenic variants (n = 34, 19.2%) or VUS (n = 11, 6.2%). SLC20A2 provided the highest contribution (16.9%), followed by XPR1 and PDGFB (3.4% each), and PDGFRB (1.7%). A total of 81.5% of carriers were symptomatic and the most recurrent symptoms were parkinsonism, cognitive impairment, and psychiatric disturbances (52.3%, 40.9%, and 38.6% of symptomatic individuals, respectively), with a wide range of age at onset (from childhood to 81 years). While the pathogenic and likely pathogenic variants identified in this study can be used for genetic counseling, the VUS will require additional evidence, such as recurrence in unrelated patients, in order to be classified as pathogenic

The role of clinical and neuroimaging features in the diagnosis of CADASIL.

BACKGROUND: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most common familial cerebral small vessel disease, caused by NOTCH3 gene mutations. The aim of our study was to identify clinical and neuroradiological features which would be useful in identifying which patients presenting with lacunar stroke and TIA are likely to have CADASIL. METHODS: Patients with lacunar stroke or TIA were included in the present study. For each patient, demographic and clinical data were collected. MRI images were centrally analysed for the presence of lacunar infarcts, microbleeds, temporal lobe involvement, global atrophy and white matter hyperintensities. RESULTS: 128 patients (mean age 56.3 ± 12.4 years) were included. A NOTCH3 mutation was found in 12.5% of them. A family history of stroke, the presence of dementia and external capsule lesions on MRI were the only features significantly associated with the diagnosis of CADASIL. Although thalamic, temporal pole gliosis and severe white matter hyperintensities were less specific for CADASIL diagnosis, the combination of a number of these factors together with familial history for stroke result in a higher positive predictive value and specificity. CONCLUSIONS: A careful familial history collection and neuroradiological assessment can identify patients in whom NOTCH3 genetic testing has a higher yield.The Lombardia GENS project has received funding from the Regione Lombardia Government as a Research Independent Project (DGR n°VIII/006128-12/12/2007). Lombardia GENS is an investigator-driven, academic, non-profit consortium and is publicly funded. Hugh Markus is supported by an NIHR Senior Investigator award and his work is supported by the Cambridge University Hospitals NIHR Biomedical Research Centr