Advanced Statistical Methods for Astrophysical Probes of Cosmology

This thesis explores advanced Bayesian statistical methods for extracting key information for cosmological model selection, parameter inference and forecasting from astrophysical observations. Bayesian model selection provides a measure of how good models in a set are relative to each other - but wh...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: March, Marisa Cristina (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Σειρά:Springer Theses, Recognizing Outstanding Ph.D. Research,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a March, Marisa Cristina.  |e author. 
245 1 0 |a Advanced Statistical Methods for Astrophysical Probes of Cosmology  |h [electronic resource] /  |c by Marisa Cristina March. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2013. 
300 |a XX, 180 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
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490 1 |a Springer Theses, Recognizing Outstanding Ph.D. Research,  |x 2190-5053 
505 0 |a Introduction -- Cosmology background -- Dark energy and apparent late time acceleration -- Supernovae Ia -- Statistical techniques -- Bayesian Doubt: Should we doubt the Cosmological Constant? -- Bayesian parameter inference for SNeIa data -- Robustness to Systematic Error for Future Dark Energy Probes -- Summary and Conclusions -- Index. 
520 |a This thesis explores advanced Bayesian statistical methods for extracting key information for cosmological model selection, parameter inference and forecasting from astrophysical observations. Bayesian model selection provides a measure of how good models in a set are relative to each other - but what if the best model is missing and not included in the set? Bayesian Doubt is an approach which addresses this problem and seeks to deliver an absolute rather than a relative measure of how good a model is.   Supernovae type Ia were the first astrophysical observations to indicate the late time acceleration of the Universe - this work presents a detailed Bayesian Hierarchical Model to infer the cosmological parameters (in particular dark energy) from observations of these supernovae type Ia. 
650 0 |a Physics. 
650 0 |a Observations, Astronomical. 
650 0 |a Astronomy  |x Observations. 
650 0 |a Cosmology. 
650 0 |a Statistical physics. 
650 0 |a Dynamical systems. 
650 0 |a Statistics. 
650 1 4 |a Physics. 
650 2 4 |a Cosmology. 
650 2 4 |a Astronomy, Observations and Techniques. 
650 2 4 |a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 
650 2 4 |a Statistical Physics, Dynamical Systems and Complexity. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783642350597 
830 0 |a Springer Theses, Recognizing Outstanding Ph.D. Research,  |x 2190-5053 
856 4 0 |u http://dx.doi.org/10.1007/978-3-642-35060-3  |z Full Text via HEAL-Link 
912 |a ZDB-2-PHA 
950 |a Physics and Astronomy (Springer-11651)