Advanced Statistical Methods for Astrophysical Probes of Cosmology

By (author) March, Marisa Cristina

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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  • 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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書名 Advanced Statistical Methods for Astrophysical Probes of Cosmology
著作者等 March, Marisa Cristina
シリーズ名 Springer Theses
出版元 Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
刊行年月 2013.01.12
ページ数 204p
大きさ H235 x W155
ISBN 9783642350597
言語 英語
出版国 ドイツ