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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Science: general issues --- Neurosciences --- Multimodal machine learning for robotics --- Deep learning for robotics --- Bayesian modeling for high-level cognitive capabilities --- Language acquisition and Symbol grounding --- Human-robot communication and collaboration based on machine learning --- Multimodal machine learning for robotics --- Deep learning for robotics --- Bayesian modeling for high-level cognitive capabilities --- Language acquisition and Symbol grounding --- Human-robot communication and collaboration based on machine learning

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• Reference Manager
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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Multimodal machine learning for robotics --- Deep learning for robotics --- Bayesian modeling for high-level cognitive capabilities --- Language acquisition and Symbol grounding --- Human-robot communication and collaboration based on machine learning

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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.

Statistics. --- Mathematical statistics—Data processing. --- Forensic sciences. --- Medical jurisprudence. --- Forensic psychology. --- Social sciences—Statistical methods. --- Statistical Theory and Methods. --- Statistics and Computing. --- Forensic Science. --- Forensic Medicine. --- Forensic Psychology. --- Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. --- Juridical psychology --- Juristic psychology --- Legal psychology --- Psychology, Forensic --- Forensic sciences --- Psychology, Applied --- Forensic medicine --- Injuries (Law) --- Jurisprudence, Medical --- Legal medicine --- Medicine --- Medical laws and legislation --- Criminalistics --- Forensic science --- Science --- Criminal investigation --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Bayes factor --- scientific evidence --- decision making --- forensic science --- uncertainty management --- probability theory --- forensic --- decision analysis --- Bayesian modeling --- R --- Bayesian statistics --- probabilistic inference --- Estadística bayesiana --- Processament de dades --- Criminalística --- R (Llenguatge de programació) --- GNU-S (Llenguatge de programació) --- Llenguatges de programació --- Ciències criminalístiques --- Ciències forenses --- Policia científica --- Policia tècnica --- Ciència --- Investigació criminal --- Economia forense --- Enginyeria forense --- Geologia forense --- Lingüística forense --- Psicologia forense --- Processament de dades electròniques --- Processament automàtic de dades --- Processament electrònic de dades --- Processament integrat de dades --- Sistematització de dades (Ordinadors) --- Tractament de dades --- Tractament electrònic de dades --- Tractament integrat de dades --- Automatització --- Informàtica --- Complexitat computacional --- Curació de dades --- Depuració (Informàtica) --- Estructures de dades (Informàtica) --- Gestió de bases de dades --- Informàtica mòbil --- Informàtica recreativa --- Intel·ligència artificial --- Sistemes en línia --- Temps real (Informàtica) --- Tractament del llenguatge natural (Informàtica) --- Processament òptic de dades --- Protecció de dades --- Transmissió de dades --- Tolerància als errors (Informàtica) --- Estadística de Bayes --- Fórmula de Bayes --- Presa de decisions (Estadística bayesiana) --- Solució de Bayes --- Teorema de Bayes --- Teoria de la decisió estadística bayesiana --- Presa de decisions