| Titre : |
Fundamentals of nonparametric Bayesian inference |
| Type de document : |
texte imprimé |
| Auteurs : |
Subhashis Ghosal, Auteur ; Aad W. van der Vaart (1959-....), Auteur |
| Editeur : |
Cambridge : Cambridge University Press |
| Année de publication : |
2017 |
| Collection : |
Cambridge series in statistical and probabilistic mathematics, ISSN 2633-0199 num. 44 |
| Importance : |
1 vol. (XXIV-646 p.) |
| Présentation : |
ill., fig., couv. ill. en coul. |
| Format : |
27 cm |
| ISBN/ISSN/EAN : |
978-0-521-87826-5 |
| Note générale : |
Contient des problèmes en fin de chapitre. - PPN 203973690 .- ISBN 0-521-87826-8 (rel.) - ISBN 978-0-521-87826-5 (hardback) |
| Langues : |
Anglais (eng) |
| Tags : |
Statistique non paramétrique Prise de décision (statistique) Statistique bayésienne Gauss, Loi de (statistique) Nonparametric statistics Bayesian statistical decision theory Gaussian processes |
| Index. décimale : |
519.542 Théorie de la décision (statistique mathématique dont statistique bayésienne) |
| Résumé : |
Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics |
| Note de contenu : |
Glossaire des symboles p. xxi. - Bibliogr. p. 623-637. Index auteur p. 138-641 - Index sujet p. 642-646. |
Fundamentals of nonparametric Bayesian inference [texte imprimé] / Subhashis Ghosal, Auteur ; Aad W. van der Vaart (1959-....), Auteur . - Cambridge : Cambridge University Press, 2017 . - 1 vol. (XXIV-646 p.) : ill., fig., couv. ill. en coul. ; 27 cm. - ( Cambridge series in statistical and probabilistic mathematics, ISSN 2633-0199; 44) . ISBN : 978-0-521-87826-5 Contient des problèmes en fin de chapitre. - PPN 203973690 .- ISBN 0-521-87826-8 (rel.) - ISBN 978-0-521-87826-5 (hardback) Langues : Anglais ( eng)
| Tags : |
Statistique non paramétrique Prise de décision (statistique) Statistique bayésienne Gauss, Loi de (statistique) Nonparametric statistics Bayesian statistical decision theory Gaussian processes |
| Index. décimale : |
519.542 Théorie de la décision (statistique mathématique dont statistique bayésienne) |
| Résumé : |
Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics |
| Note de contenu : |
Glossaire des symboles p. xxi. - Bibliogr. p. 623-637. Index auteur p. 138-641 - Index sujet p. 642-646. |
|
trié(s) par (Pertinence décroissant(e), Titre croissant(e)) Affiner la recherche
