Bayesian Computation with R

There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for stat...

Description complète

Enregistré dans:
Détails bibliographiques
Auteur principal : Albert Jim (Éditeur scientifique)
Format : Livre
Langue : anglais
Titre complet : Bayesian Computation with R / Jim Albert.
Édition : 1st ed. 2007.
Publié : New York, NY : Springer New York , [20..]
Cham : Springer Nature
Collection : Use R! (Internet)
Accès en ligne : Accès Nantes Université
Accès direct soit depuis les campus via le réseau ou le wifi eduroam soit à distance avec un compte @etu.univ-nantes.fr ou @univ-nantes.fr
Note sur l'URL : Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Condition d'utilisation et de reproduction : Conditions particulières de réutilisation pour les bénéficiaires des licences nationales : https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017
Reproduction de : Numérisation de l'édition de New York : Springer, cop. 2007
Contenu : An Introduction to R. to Bayesian Thinking. Single-Parameter Models. Multiparameter Models. to Bayesian Computation. Markov Chain Monte Carlo Methods. Hierarchical Modeling. Model Comparison. Regression Models. Gibbs Sampling. Using R to Interface with WinBUGS
Sujets :
Documents associés : Autre format: Bayesian computation with R
Autre format: Bayesian computation with R
Autre format: Robust controller design using normalized coprime factor plant design
LEADER 05980clm a2200781 4500
001 PPN12314678X
003 http://www.sudoc.fr/12314678X
005 20241001154500.0
010 |a 978-0-387-71385-4 
017 7 0 |a 10.1007/978-0-387-71385-4  |2 DOI 
035 |a (OCoLC)652718951 
035 |a Springer978-0-387-71385-4 
035 |a SPRINGER_EBOOKS_LN_PLURI_10.1007/978-0-387-71385-4 
035 |a Springer-11649-978-0-387-71385-4 
100 |a 20080410f20 k y0frey0103 ba 
101 0 |a eng  |2 639-2 
102 |a US 
105 |a a a 001yy 
135 |a dr||||||||||| 
181 |6 z01  |c txt  |2 rdacontent 
181 1 |6 z01  |a i#  |b xxxe## 
182 |6 z01  |c c  |2 rdamedia 
182 1 |6 z01  |a b 
183 |6 z01  |a ceb  |2 RDAfrCarrier 
200 1 |a Bayesian Computation with R  |f Jim Albert. 
205 |a 1st ed. 2007. 
214 0 |a New York, NY  |c Springer New York 
214 2 |a Cham  |c Springer Nature  |d [20..] 
225 0 |a Use R!  |x 2197-5744 
303 |a L'impression du document génère 278 p. 
320 |a Bibliogr. Index 
324 |a Numérisation de l'édition de New York : Springer, cop. 2007 
327 1 |a An Introduction to R  |a to Bayesian Thinking  |a Single-Parameter Models  |a Multiparameter Models  |a to Bayesian Computation  |a Markov Chain Monte Carlo Methods  |a Hierarchical Modeling  |a Model Comparison  |a Regression Models  |a Gibbs Sampling  |a Using R to Interface with WinBUGS 
330 |a There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples. This book is a suitable companion book for an introductory course on Bayesian methods and is valuable to the statistical practitioner who wishes to learn more about the R language and Bayesian methodology. The LearnBayes package, written by the author and available from the CRAN website, contains all of the R functions described in the book. Jim Albert is Professor of Statistics at Bowling Green State University. He is Fellow of the American Statistical Association and is past editor of The American Statistician. His books include Ordinal Data Modeling (with Val Johnson), Workshop Statistics: Discovery with Data, A Bayesian Approach (with Allan Rossman), and Bayesian Computation using Minitab 
371 0 |a Accès en ligne pour les établissements français bénéficiaires des licences nationales 
371 0 |a Accès soumis à abonnement pour tout autre établissement 
371 1 |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales  |c https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 
410 | |0 159292360  |t Use R! (Internet)  |x 2197-5744 
452 | |0 118836293  |t Bayesian computation with R  |f Jim Albert  |c New York  |n Springer  |d 2007  |p 1 vol. (X-267 p.)  |s Use R!  |y 978-0-387-71384-7 
452 | |0 118836293  |t Bayesian computation with R  |f Jim Albert  |c New York  |n Springer  |d 2007  |p 1 vol. (X-267 p.)  |s Use R!  |y 978-0-387-71384-7 
452 | |0 021312591  |t Robust controller design using normalized coprime factor plant design  |f D.C. McFarlane, K. Glover  |c Berlin  |n Springer-Verlag  |d 1990  |p 1 vol. (X-206 p.)  |s Lecture notes in control and information sciences  |y 0-387-51851-7 
605 |3 PPN08080859X  |a R  |n logiciel  |2 rameau 
606 |3 PPN027238660  |a Markov, Processus de  |2 rameau 
606 |3 PPN027296539  |a Statistique mathématique  |3 PPN027234886  |x Informatique  |2 rameau 
606 |3 PPN029753090  |a Statistique bayésienne  |3 PPN027234886  |x Informatique  |2 rameau 
610 1 |a Statistics 
610 2 |a Statistics and Computing/Statistics Programs 
610 2 |a Simulation and Modeling 
610 2 |a Computational Mathematics and Numerical Analysis 
610 2 |a Visualization 
610 2 |a Optimization 
615 |a Mathematics and Statistics  |n 11649  |2 Springer 
676 |a 519.5  |v 23 
680 |a QA276-280 
686 |a 62F15  |c 2010  |2 msc 
686 |a 62C10  |c 2010  |2 msc 
700 1 |3 PPN11863481X  |a Albert  |b Jim  |f 1953-....  |4 070 
702 1 |a Albert  |b Jim  |4 340 
801 3 |a FR  |b Abes  |c 20240911  |g AFNOR 
801 1 |a DE  |b Springer  |c 20211104  |g AACR2 
856 4 |q PDF  |u https://doi.org/10.1007/978-0-387-71385-4  |z Accès sur la plateforme de l'éditeur 
856 4 |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-HPDXJR64-M  |z Accès sur la plateforme Istex 
856 4 |5 441099901:83084452X  |u https://budistant.univ-nantes.fr/login?url=https://doi.org/10.1007/978-0-387-71385-4 
915 |5 441099901:83084452X  |b SPRING18-00079 
930 |5 441099901:83084452X  |b 441099901  |j g 
991 |5 441099901:83084452X  |a Exemplaire créé en masse par ITEM le 30-09-2024 15:58 
997 |a NUM  |b SPRING18-00079  |d NUMpivo  |e EM  |s d 
998 |a 977628