Prediction in forensic science: a critical examination of common understandings

In this commentary, we argue that the term 'prediction' is overly used when in fact, referring to foundational writings of de Finetti, the correspondent term should be inference. In particular, we intend (i) to summarize and clarify relevant subject matter on prediction from established statistical theory, and (ii) point out the logic of this understanding with respect practical uses of the term prediction. Written from an interdisciplinary perspective, associating statistics and forensic science as an example, this discussion also connects to related fields such as medical diagnosis and other areas of application where reasoning based on scientific results is practiced in societal relevant contexts. This includes forensic psychology that uses prediction as part of its vocabulary when dealing with matters that arise in the course of legal proceedings

Pion scattering in Wilson ChPT

We compute the scattering amplitude for pion scattering in Wilson chiral perturbation theory for two degenerate quark flavors. We consider two different regimes where the quark mass m is of order (i) a\Lambda_QCD^2 and (ii) a^2\Lambda_QCD^3. Analytic expressions for the scattering lengths in all three isospin channels are given. As a result of the O(a^2) terms the I=0 and I=2 scattering lengths do not vanish in the chiral limit. Moreover, additional chiral logarithms proportional to a^2\ln M_{\pi}^2 are present in the one-loop results for regime (ii). These contributions significantly modify the familiar results from continuum chiral perturbation theory.Comment: 20 pages, 4 figures. V3: Comments on finite size effects and the axial vector current added, one more reference. To be published in PR

La naturaleza decisoria de las conclusiones de los expertos en ciencia forense (The decisionalization of individualization)

En la ciencia forense y ramas de la ciencia adyacentes, tanto investigadores del ámbito académico como quienes las practican continúan divergiendo en la percepción y comprensión del término “individualización”, es decir, la defensa de la tesis de que es posible reducir un conjunto de potenciales donantes de un vestigio forense a una única fuente. En concreto, se ha puesto de manifiesto que recientes cambios que entienden la práctica de la individualización como una decisión no son más que un mero cambio de etiqueta [1], dejando los cambios fundamentales en el orden del pensar y del entender aún pendientes. Es más, asociaciones profesionales y expertos huyen de adherirse a la noción de decisión tal y como la define la teoría formal de la decisión en la que la individualización puede contextualizarse, principalmente por las dificultades para tratar sobre las medidas de deseabilidad o no de las consecuencias de las decisiones (por ejemplo, utilizando las funciones de utilidad). Apoyándose en investigaciones existentes en esta área, este artículo presenta y discute sobre conceptos fundamentales de utilidades y costes, con particular referencia a su aplicación a la individualización forense. El artículo subraya que una adecuada comprensión de las herramientas de la decisión no solo reduce el número de asignaciones individuales que la aplicación de la teoría de la decisión requiere, sino que también muestra cómo esas asignaciones pueden relacionarse significativamente con las propiedades constituyentes del problema de la decisión en el mundo real al que se aplica la teoría. Se argumenta que la “decisionalización” de la individualización requiere esa percepción fundamental para iniciar cambios en las comprensiones subyacentes de esos campos, no meramente en el ámbito de sus etiquetas

Optimal design when outcome values are not missing at random

The presence of missing values complicates statistical analyses. In design of experiments, missing values are particularly problematic when constructing optimal designs, as it is not known which values are missing at the design stage. When data are missing at random it is possible to incorporate this information into the optimality criterion that is used to find designs; Imhof, Song and Wong (2002) develop such a framework. However, when data are not missing at random this framework can lead to inefficient designs. We investigate and address the specific challenges that not missing at random values present when finding optimal designs for linear regression models. We show that the optimality criteria will depend on model parameters that traditionally do not affect the design, such as regression coefficients and the residual variance. We also develop a framework that improves efficiency of designs over those found assuming values are missing at random

Optimal design for experiments with possibly incomplete observations

Missing responses occur in many industrial or medical experiments, for example in clinical trials where slow acting treatments are assessed. Finding efficient designs for such experiments can be problematic since it is not known at the design stage which observations will be missing. The design literature mainly focuses on assessing robustness of designs for missing data scenarios, rather than finding designs which are optimal in this situation. Imhof, Song and Wong (2002) propose a framework for design search, based on the expected information matrix. We develop a new approach which includes Imhof, Song and Wong (2002)'s method as special case and justifies its use retrospectively. Our method is illustrated through a simulation study based on real data from an Alzheimer's disease trial

Opportunities for farming in alpine countries – pathways to truly grassland-based beef and milk production in Austria and Switzerland

Farming in the alpine countries of Austria and Switzerland fulfils important economic, socio-cultural and ecological functions for society. However, even though both Austria and Switzerland have increasingly focused their agricultural policy towards ecology, in both countries negative environmental impacts of agriculture still have to be reduced massively

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