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Planning (firm) --- Mathematical statistics --- Bayesian statistical decision theory --- Statistique bayésienne --- Bayesian statistical decision theory. --- Statistique bayésienne --- Statistique bayésienne. --- Statistique mathématique. --- Mathematical statistics. --- Statistique bayésienne. --- Statistique mathématique.

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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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We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and frequentist ideas. Estimation, testing and prediction blend in this framework, producing opportunities for new methodologies of increased power. New difficulties also arise, easily leading to flawed inferences. This book takes a careful look at both the promise and pitfalls of large-scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Emphasis is on the inferential ideas underlying technical developments, illustrated using a large number of real examples.

Bayesian statistical decision theory. --- 519.2 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Bayesian statistical decision theory --- Statistique bayésienne --- Mathematical statistics. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods

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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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Given a data set, you can fit thousands of models at the push of a button, but how do you choose the best? With so many candidate models, overfitting is a real danger. Is the monkey who typed Hamlet actually a good writer? Choosing a model is central to all statistical work with data. We have seen rapid advances in model fitting and in the theoretical understanding of model selection, yet this book is the first to synthesize research and practice from this active field. Model choice criteria are explained, discussed and compared, including the AIC, BIC, DIC and FIC. The uncertainties involved with model selection are tackled, with discussions of frequentist and Bayesian methods; model averaging schemes are presented. Real-data examples are complemented by derivations providing deeper insight into the methodology, and instructive exercises build familiarity with the methods. The companion website features Data sets and R code.

Mathematical statistics --- -Bayesian statistical decision theory. --- Bayesian statistical decision theory --- Mathematical models --- 519.5 --- Academic collection --- 519.2 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Models, Mathematical --- Simulation methods --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Research --- Statistical methods --- Bayesian statistical decision theory. --- Research. --- Statistique bayésienne --- Mathematical Sciences --- Probability --- Mathematical models - Research --- Mathematical statistics - Research