An Introduction to Bayesian analysis : theory and methods
This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be...
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| Auteurs principaux: | , , |
|---|---|
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Édition: | 1st ed. 2006. |
| Collection: | Springer Texts in Statistics
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| Sujets: | |
| Accès en ligne: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Note: |
Description d'après consultation du 02 mars 2011 Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • An Introduction to bayesian analysis, theory and methods, Jayanta K. Ghosh, Mohan Delampady, Tapas Samanta, New York, Springer, 2006, 1 vol. (XIII-352 p.), Springer Texts in Statistics, 0-387-40084-2 • Molecular mechanisms of hormone action, 40. Colloquium der Gesellschaft für Biologische Chemie, 6-8 April 1989 in Mosbach/Baden, ed. by U. Gehring, E. Helmreich and G. Schultz, Berlin, Springer, 1989, XII-204 p, 3-540-51607-7 • An introduction to Bayesian analysis, theory and methods, Jayanta K. Ghosh, Mohan Delampady, Tapas Samanta, New York, Springer, 2010, 1 vol. (XIII-352 p.), Springer texts in statistics, 978-1-441-92303-5 • An Introduction to bayesian analysis, theory and methods, Jayanta K. Ghosh, Mohan Delampady, Tapas Samanta, New York, Springer, 2006, 1 vol. (XIII-352 p.), Springer Texts in Statistics, 0-387-40084-2 |
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| 100 | 1 | |a Ghosh, Jayanta K., |d 1937-2017. | |
| 245 | 1 | 0 | |a An Introduction to Bayesian analysis : |b theory and methods |c edited by Jayanta K. Ghosh, Mohan Delampady, Tapas Samanta. |
| 250 | |a 1st ed. 2006. | ||
| 260 | |a New York, NY : |b Springer New York. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Springer Texts in Statistics |x 2197-4136 | |
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| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a Statistical Preliminaries Bayesian Inference and Decision Theory Utility, Prior, and Bayesian Robustness Large Sample Methods Choice of Priors for Low-dimensional Parameters Hypothesis Testing and Model Selection Bayesian Computations Some Common Problems in Inference High-dimensional Problems Some Applications | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical computing. Advances in both low-dimensional and high-dimensional problems are covered, as well as important topics such as empirical Bayes and hierarchical Bayes methods and Markov chain Monte Carlo (MCMC) techniques. Many topics are at the cutting edge of statistical research. Solutions to common inference problems appear throughout the text along with discussion of what prior to choose. There is a discussion of elicitation of a subjective prior as well as the motivation, applicability, and limitations of objective priors. By way of important applications the book presents microarrays, nonparametric regression via wavelets as well as DMA mixtures of normals, and spatial analysis with illustrations using simulated and real data. Theoretical topics at the cutting edge include high-dimensional model selection and Intrinsic Bayes Factors, which the authors have successfully applied to geological mapping. The style is informal but clear. Asymptotics is used to supplement simulation or understand some aspects of the posterior. J.K. Ghosh has been Director and Jawaharlal Nehru Professor at the Indian Statistical Institute and President of the International Statistical Institute. He is currently a professor of statistics at Purdue University and professor emeritus at the Indian Statistical Institute. He has been the editor of Sankhya and has served on the editorial boards of several journals including the Annals of Statistics. His current interests in Bayesian analysis include asymptotics, nonparametric methods, high-dimensional model selection, reliability and survival analysis, bioinformatics, astrostatistics and sparse and not so sparse mixtures. Mohan Delampady and Tapas Samanta are both professors of statistics at the Indian Statistical Institute and both are interested in Bayesian inference, specifically in topics such as model selection, asymptotics, robustness and nonparametrics | ||
| 650 | |a Statistique bayésienne | ||
| 650 | |a Prise de décision (statistique) | ||
| 650 | |a Méthodes statistiques | ||
| 700 | 1 | |a Delampady, Mohan. |4 aut | |
| 700 | 1 | |a Samanta, Tapas. |4 aut | |
| 700 | 1 | |a Delampady, Mohan. |4 aut | |
| 700 | 1 | |a Samanta, Tapas. |4 aut | |
| 776 | 0 | |0 109084918 |t An Introduction to bayesian analysis |o theory and methods |f Jayanta K. Ghosh, Mohan Delampady, Tapas Samanta |c New York |n Springer |d 2006 |p 1 vol. (XIII-352 p.) |s Springer Texts in Statistics |z 0-387-40084-2 | |
| 776 | 0 | |0 006928978 |t Molecular mechanisms of hormone action |o 40. Colloquium der Gesellschaft für Biologische Chemie, 6-8 April 1989 in Mosbach/Baden |f ed. by U. Gehring, E. Helmreich and G. Schultz |c Berlin |n Springer |d 1989 |p XII-204 p |z 3-540-51607-7 | |
| 776 | 0 | |0 152954902 |t An introduction to Bayesian analysis |o theory and methods |f Jayanta K. Ghosh, Mohan Delampady, Tapas Samanta |c New York |n Springer |d 2010 |p 1 vol. (XIII-352 p.) |s Springer texts in statistics |z 978-1-441-92303-5 | |
| 776 | 0 | |0 109084918 |t An Introduction to bayesian analysis |o theory and methods |f Jayanta K. Ghosh, Mohan Delampady, Tapas Samanta |c New York |n Springer |d 2006 |p 1 vol. (XIII-352 p.) |s Springer Texts in Statistics |z 0-387-40084-2 | |
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