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Fundamentals of nonparametric Bayesian inference / Subhashis Ghosal (2017)

Public ISBD 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.

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Model-based clustering and classification for data science / Charles Bouveyron (2019)

Public ISBD Titre : Model-based clustering and classification for data science : with applications in R Type de document : texte imprimé Auteurs : Charles Bouveyron (1979-....), Auteur ; Gilles Celeux, Auteur ; Thomas Brendan Murphy, Auteur ; Adrian E. Raftery Editeur : Cambridge : Cambridge University Press Année de publication : 2019 Collection : Cambridge series in statistical and probabilistic mathematics, ISSN 2633-0199 Importance : 1 vol. (xvii-427 p.) Présentation : ill., couv. ill. en coul. ; 26 cm Format : 26 cm ISBN/ISSN/EAN : 978-1-108-49420-5 Note générale : PPN 240418786 Langues : Anglais (eng) Tags : R (logiciel)  Classification automatique (statistique)  Analyse des données  Modèles mathématiques  R (Computer program language)  Cluster analysis  Mathematical statistics  Statistics -- Classification Index. décimale : 519.53 Statistiques descriptives, analyse multivariée, analyse de la variance et de la covariance Résumé : Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as : how many clusters are there ? Which method should I use ? How should I handle outliers ? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code ; describes modern approaches to high-dimensional data and networks ; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics. Note de contenu : Bibliogr. p. [386]-414. Index Model-based clustering and classification for data science : with applications in R [texte imprimé] / Charles Bouveyron (1979-....), Auteur ; Gilles Celeux, Auteur ; Thomas Brendan Murphy, Auteur ; Adrian E. Raftery . - Cambridge : Cambridge University Press, 2019 . - 1 vol. (xvii-427 p.) : ill., couv. ill. en coul. ; 26 cm ; 26 cm. - (Cambridge series in statistical and probabilistic mathematics, ISSN 2633-0199) . ISBN : 978-1-108-49420-5 PPN 240418786 Langues : Anglais (eng) Tags : R (logiciel)  Classification automatique (statistique)  Analyse des données  Modèles mathématiques  R (Computer program language)  Cluster analysis  Mathematical statistics  Statistics -- Classification Index. décimale : 519.53 Statistiques descriptives, analyse multivariée, analyse de la variance et de la covariance Résumé : Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as : how many clusters are there ? Which method should I use ? How should I handle outliers ? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code ; describes modern approaches to high-dimensional data and networks ; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics. Note de contenu : Bibliogr. p. [386]-414. Index

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