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Bayesian statistical decision theory. --- Estadística bayesiana --- Bayes' solution --- Bayesian analysis --- Statistical decision --- 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

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Estadística bayesiana --- 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 --- Bayesian statistical decision theory. --- Reliability (Engineering) --- Survival analysis (Biometry) --- Analysis, Survival (Biometry) --- Survivorship analysis (Biometry) --- Biometry --- Failure time data analysis --- Reliability of equipment --- Systems reliability --- Engineering --- Maintainability (Engineering) --- Probabilities --- Systems engineering --- Plant performance --- Safety factor in engineering --- Structural failures --- Bayes' solution --- Bayesian analysis --- Statistical decision

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Bayesian analysis is one of the important tools for statistical modelling and inference. Bayesian frameworks and methods have been successfully applied to solve practical problems in reliability and survival analysis, which have a wide range of real world applications in medical and biological sciences, social and economic sciences, and engineering. In the past few decades, significant developments of Bayesian inference have been made by many researchers, and advancements in computational technology and computer performance has laid the groundwork for new opportunities in Bayesian computation for practitioners. Because these theoretical and technological developments introduce new questions and challenges, and increase the complexity of the Bayesian framework, this book brings together experts engaged in groundbreaking research on Bayesian inference and computation to discuss important issues, with emphasis on applications to reliability and survival analysis. Topics covered are timely and have the potential to influence the interacting worlds of biostatistics, engineering, medical sciences, statistics, and more. The included chapters present current methods, theories, and applications in the diverse area of biostatistical analysis. The volume as a whole serves as reference in driving quality global health research. .

Bayesian statistical decision theory. --- Reliability (Engineering) --- Survival analysis (Biometry) --- Analysis, Survival (Biometry) --- Survivorship analysis (Biometry) --- Biometry --- Failure time data analysis --- Reliability of equipment --- Systems reliability --- Engineering --- Maintainability (Engineering) --- Probabilities --- Systems engineering --- Plant performance --- Safety factor in engineering --- Structural failures --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Estadística bayesiana --- 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 --- Statistics. --- Clinical medicine --- Bayesian Inference. --- Bayesian Network. --- Clinical Research. --- Research. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics

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Reconeixement de formes (Informàtica) --- Visió per ordinador --- Models matemàtics --- Processos de Markov --- Estadística bayesiana --- 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 --- Processos estocàstics --- Moviment brownià --- Processos de difusió --- Processos de moviment brownià --- Models (Matemàtica) --- Models experimentals --- Models teòrics --- Mètodes de simulació --- Anàlisi de sistemes --- Mètode de Montecarlo --- Modelització multiescala --- Models economètrics --- Models lineals (Estadística) --- Models multinivell (Estadística) --- Models no lineals (Estadística) --- Programació (Ordinadors) --- Simulació per ordinador --- Teoria de màquines --- Models biològics --- Intel·ligència artificial --- Processament d'imatges --- Autòmats cel·lulars --- Reconeixement automàtic de la parla --- Markov processes. --- Bayesian statistical decision theory. --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Stochastic processes

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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