000			02116cam a2200301zu 4500
001			88863538
003			FRCYB88863538
005			20250107230928.0
006			m o d
007			cr un
008			250108s2018 fr | o|||||0|0|||eng d
020			_a9782759822744
035			_aFRCYB88863538
040			_aFR-PaCSA _ben _c _erda
100	1		_aFraix-Burnet, Didier
245	0	1	_aStatistics for Astrophysics _bBayesian Methodology _c['Fraix-Burnet, Didier ', 'Girard, Stéphane ', 'Arbel, Julyan ']
264		1	_bEDP Sciences _c2018
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aFraix-Burnet, Didier
700	0		_aGirard, Stéphane
700	0		_aArbel, Julyan
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88863538 _qtext/html _a
520			_aThis 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.
999			_c71007 _d71007