Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Quatrième de couverture : "Une formule mathématique élémentaire révolutionne la boîte à outils des chercheurs et des ingénieurs : friande de simulations, la formule de Bayes surfe sur les vagues successives du raz de marée informatique et aide à démêler les réseaux complexes de causes des défis scientifiques de ce début du troisième millénaire. Ce manuel d'initiation à la statistique bayésienne en montre la pertinence théorique et l'efficacité pratique. L'approche bayésienne synthétise naturellement différentes sources d'information (données, modèles, expertises). C'est pourquoi elle intervient de manière décisive dans de nombreux domaines, dont l'analyse des risques. Adaptée à l'analyse quantitative des incertitudes de prédiction, on la rencontre de plus en plus dans l'élaboration des modèles mathématiques utilisés en biologie, épidémiologie, écologie et agronomie, domaines de référence des auteurs. Accessible aux débutants, ce livre s'adresse en priorité aux professionnels des sciences du vivant et de l'environnement (ingénieurs, gestionnaires, chercheurs et étudiants) soucieux de la meilleure exploitation de leurs données au travers d'une démarche quantitative cohérente. La première partie de l'ouvrage présente les bases nécessaires à la statistique bayésienne : probabilités, démarche, modélisation graphique, algorithmes (y compris MCMC et ABC), évaluation des modèles, définition des lois a priori. La seconde développe plusieurs exemples réels et propose des cas d'études. Glossaire et index complètent l'ouvrage. Les codes WinBUGS et données utilisés sont disponibles sur le site web de l'ouvrage."

Bayes Theorem --- Probability --- Risk Assessment --- Epidemiology

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. The stakes are high and the reader determines the outcome. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. This works in conjunction with the bayess package. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels, as exemplified by courses given at Université Paris Dauphine (France), University of Canterbury (New Zealand), and University of British Columbia (Canada). It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics. A strength of the text is the noteworthy emphasis on the role of models in statistical analysis. This is the new, fully-revised edition to the book Bayesian Core: A Practical Approach to Computational Bayesian Statistics. .

Bayesian statistical decision theory. --- R (Computer program language) --- GNU-S (Computer program language) --- Domain-specific programming languages --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Mathematical statistics. --- Statistics and Computing/Statistics Programs. --- Statistical Theory and Methods. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- R (Computer program language). --- Statistics.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. The stakes are high and the reader determines the outcome. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable.

Bayesian statistical decision theory --- R (Computer program language) --- Bayesian statistical decision theory.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. The stakes are high and the reader determines the outcome. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. This works in conjunction with the bayess package. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels, as exemplified by courses given at Université Paris Dauphine (France), University of Canterbury (New Zealand), and University of British Columbia (Canada). It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics. A strength of the text is the noteworthy emphasis on the role of models in statistical analysis. This is the new, fully-revised edition to the book Bayesian Core: A Practical Approach to Computational Bayesian Statistics. .

Statistical science --- Mathematical statistics --- Computer. Automation --- Bayesian statistics --- informatica --- statistiek --- statistisch onderzoek