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 wha...

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Detaylı Bibliyografya
Yazar: March, Marisa Cristina
Materyal Türü: Livre numérique
Dil:Anglais
Baskı/Yayın Bilgisi: Berlin, Heidelberg : Springer Berlin Heidelberg [20..].
Cham : Springer Nature
Edisyon:1st ed. 2013.
Seri Bilgileri:Springer Theses, Recognizing Outstanding Ph.D. Research
Online Erişim:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
Not: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Advanced statistical methods for astrophysical probes of cosmology, doctoral thesis accepted by the Astrophysics Group of Imperial college London, Marisa Cristina March, 2013, Berlin, Springer, 1 vol. (XX-177 p.), Springer theses, 978-3-642-35059-7
• Advanced Statistical Methods for Astrophysical Probes of Cosmology, Texte imprimé, 9783642350610
• Advanced Statistical Methods for Astrophysical Probes of Cosmology, Texte imprimé, 9783642444548
• Advanced statistical methods for astrophysical probes of cosmology, doctoral thesis accepted by the Astrophysics Group of Imperial college London, Marisa Cristina March, 2013, Berlin, Springer, 1 vol. (XX-177 p.), Springer theses, 978-3-642-35059-7
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245 1 0 |a Advanced Statistical Methods for Astrophysical Probes of Cosmology   |c by Marisa Cristina March. 
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505 1 |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 
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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 
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