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Statistics for astrophysics / Didier Fraix-Burnet (DL 2018)

Public ISBD Titre : Statistics for astrophysics : Bayesian methodology Type de document : texte imprimé Auteurs : Didier Fraix-Burnet, Editeur scientifique ; Stéphane Girard, Editeur scientifique ; Julyan Arbel, Editeur scientifique ; Jean-Baptiste Marquette, Editeur scientifique Congrès : School of statistics for astrophysics (03; 2017; Autrans, Isère), Auteur Editeur : Les Ulis ; Paris : EDP Sciences Année de publication : DL 2018 Collection : EDP Sciences proceedings Importance : 1 vol. (138 p.) : ill. en coul. ; 24 cm Présentation : illustrations en couleurs Format : 24 cm ISBN/ISSN/EAN : 978-2-7598-2274-4 Note générale : PPN 252474945 Langues : Anglais (eng) Tags : Astrophysique -- Méthodes statistiques -- Congrès  Statistique bayésienne -- Congrès  Astrophysics -- Statistical methods -- Congresses  Bayesian statistical decision theory -- Congresses Index. décimale : 520.151 95 Astronomie. Méthodes statistiques Résumé : This book includes the lectures given during the third session of the School of statistics for astrophysics that took place at Autrans, near Grenoble, in France, in October 2017. The subject is Bayesian methodology. The interest of this statistical approach in astrophysics probably comes from its necessity and its success in determining the cosmological parameters from observations, especially from the cosmic background fluctuations. The cosmological community has thus been very active in this field for many years. But the Bayesian methodology, complementary to the more classical frequentist one, has many applications in physics in general due to its ability to incorporate a priori knowledge into inference, such as uncertainty brought by the observational processes. The Bayesian approach becomes more and more widespread in the astrophysical literature. This book contains statistics courses on basic to advanced methods with practical exercises using the R environment, by leading experts in their field. This covers the foundations of Bayesian inference, Markov chain Monte Carlo technique, model building, Approximate Bayesian Computation (ABC) and Bayesian nonparametric inference and clustering. (4e de couverture) Note de contenu : Bibliographie en fin de chapitres. Statistics for astrophysics : Bayesian methodology [texte imprimé] / Didier Fraix-Burnet, Editeur scientifique ; Stéphane Girard, Editeur scientifique ; Julyan Arbel, Editeur scientifique ; Jean-Baptiste Marquette, Editeur scientifique / School of statistics for astrophysics (03; 2017; Autrans, Isère), Auteur . - Les Ulis ; Paris : EDP Sciences, DL 2018 . - 1 vol. (138 p.) : ill. en coul. ; 24 cm : illustrations en couleurs ; 24 cm. - (EDP Sciences proceedings) . ISBN : 978-2-7598-2274-4 PPN 252474945 Langues : Anglais (eng) Tags : Astrophysique -- Méthodes statistiques -- Congrès  Statistique bayésienne -- Congrès  Astrophysics -- Statistical methods -- Congresses  Bayesian statistical decision theory -- Congresses Index. décimale : 520.151 95 Astronomie. Méthodes statistiques Résumé : This book includes the lectures given during the third session of the School of statistics for astrophysics that took place at Autrans, near Grenoble, in France, in October 2017. The subject is Bayesian methodology. The interest of this statistical approach in astrophysics probably comes from its necessity and its success in determining the cosmological parameters from observations, especially from the cosmic background fluctuations. The cosmological community has thus been very active in this field for many years. But the Bayesian methodology, complementary to the more classical frequentist one, has many applications in physics in general due to its ability to incorporate a priori knowledge into inference, such as uncertainty brought by the observational processes. The Bayesian approach becomes more and more widespread in the astrophysical literature. This book contains statistics courses on basic to advanced methods with practical exercises using the R environment, by leading experts in their field. This covers the foundations of Bayesian inference, Markov chain Monte Carlo technique, model building, Approximate Bayesian Computation (ABC) and Bayesian nonparametric inference and clustering. (4e de couverture) Note de contenu : Bibliographie en fin de chapitres.

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