Statistics for Astrophysics : : Bayesian Methodology / / Didier Fraix‐Burnet, Julyan Arbel, Stéphane Girard, Jean-Baptiste Marquette.

• Superior document: Title is part of eBook package: De Gruyter EDP Sciences Contemporary eBook-Package 2016-2020
• VerfasserIn: Marquette, Jean-Baptiste,; Arbel, Julyan,; Girard, Stéphane,
• MitwirkendeR: Arbel, Julyan,; Dyk, David A. van,; Robert, Christian P.,; Stenning, David C.,
• Place / Publishing House: Les Ulis : : EDP Sciences, , [2018]; ©2018
• Year of Publication: 2018
• Language: English
• Series: EDP Sciences Proceedings
• Physical Description: 1 online resource (140 p.)
• Other title: Frontmatter --; Organisers --; Lecturers --; Acknowledgments --; Table of Contents --; Foreword --; BAYESIAN STATISTICAL METHODS FOR ASTRONOMY PART I: FOUNDATIONS --; BAYESIAN STATISTICAL METHODS FOR ASTRONOMY PART II: MARKOV CHAIN MONTE CARLO --; BAYESIAN STATISTICAL METHODS FOR ASTRONOMY PART III: MODEL BUILDING --; APPROXIMATE BAYESIAN COMPUTATION, AN INTRODUCTION --; CLUSTERING MILKY WAY’S GLOBULAR CLUSTERS: A BAYESIAN NONPARAMETRIC APPROACH
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9782759822751; 9783110756401; 9783111023885
• DOI: 10.1051/978-2-7598-2275-1
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: Didier Fraix‐Burnet, Julyan Arbel, Stéphane Girard, Jean-Baptiste Marquette.

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 luctuations. 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.