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Statistique bayésienne --- Statistique mathematique --- Regression

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Pratique du calcul bayésien est né de l'expérience acquise lors des cours donnés en sciences de l'environnement, tant à l'université de Liège (Arlon), qu'à la grande école AgroParisTech (Paris). Son fil conducteur peut se résumer par la locution « de la plume à la souris », tournure empruntée à un opuscule retraçant la vie d'une école fréquentée jadis par le premier auteur. La première partie privilégie les modèles statistiques paramétriques calculables « à la plume » et cependant très riches, tant du point de vue de la présentation des concepts fondateurs du paradigme bayésien, que de leurs applications opérationnelles, notamment en matière d'aide à la décision. Dès le premier chapitre, la représentation du modèle par un graphe acyclique orienté permet de distinguer clairement la phase où la créativité du chercheur s'exprime de celle où il calcule. À cette fin, le logiciel libre WinBUGS sera très utile à l'apprenti modélisateur. La seconde partie présente des applications réelles, plus sophistiquées, qui nécessitent souvent d'introduire une couche de variables latentes entre les observables et les paramètres. Conduire une inférence bayésienne sur ces modèles hiérarchiques implique un recours intensif aux méthodes modernes de calcul et mobilise donc « la souris » de l'ordinateur. Cet ouvrage est dédié aux étudiants et chercheurs qui souhaitent apprendre le calcul bayésien avec des visées opérationnelles. Le lecteur est invité à l'utiliser comme un tremplin lui permettant d'aller aussi loin que son intérêt et/ou ses besoins l'exigent. C'est pourquoi, les treize chapitres offrent un compromis entre la rigueur du langage mathématique et la souplesse de la langue de Molière. Le côté opérationnel est mis en avant. De nombreux exemples, le plus souvent réels, justifient les efforts et illustrent les raisonnements sous-jacents. Les développements théoriques sont donc volontairement limités à l'essentiel et le lecteur désireux de les poursuivre trouvera deux ouvrages de référence publiés dans la même collection.

Bayesian statistical decision theory --- Statistics --- Probabilities --- Statistique bayésienne --- Statistique --- Probabilités --- EPUB-LIV-FT LIVMATHE LIVSTATI SPRINGER-B

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The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics. Rather than getting bogged down

Mathematical statistics --- Probability theory --- Biomathematics. Biometry. Biostatistics --- Molecular biology --- Bioinformatics. --- Bayesian statistical decision theory. --- Probabilities. --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Risk --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Data processing --- Bioinformatics --- Bayesian statistical decision theory --- Probabilities --- Bio-informatique --- Statistique bayésienne --- Probabilités

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This book provides a compact self-contained introduction to the theory and application of Bayesian statistical methods. The book is accessible to readers having a basic familiarity with probability, yet allows more advanced readers to quickly grasp the principles underlying Bayesian theory and methods. The examples and computer code allow the reader to understand and implement basic Bayesian data analyses using standard statistical models and to extend the standard models to specialized data analysis situations. The book begins with fundamental notions such as probability, exchangeability and Bayes' rule, and ends with modern topics such as variable selection in regression, generalized linear mixed effects models, and semiparametric copula estimation. Numerous examples from the social, biological and physical sciences show how to implement these methodologies in practice. Monte Carlo summaries of posterior distributions play an important role in Bayesian data analysis. The open-source R statistical computing environment provides sufficient functionality to make Monte Carlo estimation very easy for a large number of statistical models and example R-code is provided throughout the text. Much of the example code can be run ``as is'' in R, and essentially all of it can be run after downloading the relevant datasets from the companion website for this book. Peter Hoff is an Associate Professor of Statistics and Biostatistics at the University of Washington. He has developed a variety of Bayesian methods for multivariate data, including covariance and copula estimation, cluster analysis, mixture modeling and social network analysis. He is on the editorial board of the Annals of Applied Statistics.

informatietechnologie --- methodologieën --- database management --- Information systems --- Social sciences (general) --- Operational research. Game theory --- statistisch onderzoek --- speltheorie --- econometrie --- operationeel onderzoek --- sociale wetenschappen --- Quantitative methods (economics) --- Statistical science --- stochastische analyse --- Distribution (Probability theory. --- Mathematical statistics. --- Social sciences --- Computer science. --- Econometrics. --- Methodology. --- Probability Theory and Stochastic Processes. --- Operations Research, Management Science. --- Statistical Theory and Methods. --- Methodology of the Social Sciences. --- Probability and Statistics in Computer Science. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Distribution functions --- Frequency distribution --- Characteristic functions --- Economics, Mathematical --- Informatics --- Science --- Statistical methods --- Bayesian statistical decision theory --- Data analysis --- Bayesian statistical decision theory. --- Statistical methods. --- Mathematical statistics --- Programming --- Probabilities. --- Operations research. --- Management science. --- Statistics . --- Social sciences. --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Statistical analysis --- Statistical data --- Econometrics --- Quantitative business analysis --- Management --- Problem solving --- Operations research --- Statistical decision --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Probability --- Combinations --- Chance --- Least squares --- Risk --- Statistique bayésienne --- Social sciences - Statistical methods --- Statistics.