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Advanced Statistical Methods for Astrophysical Probes of Cosmology
Marisa Cristina March
出版
Springer Science & Business Media
, 2013-01-13
主題
Science / Space Science / Cosmology
Science / Space Science / Astronomy
Mathematics / Probability & Statistics / General
Science / Physics / General
Science / Physics / Mathematical & Computational
Science / Physics / Relativity
Science / Physics / Astrophysics
ISBN
3642350607
9783642350603
URL
http://books.google.com.hk/books?id=bTFEAAAAQBAJ&hl=&source=gbs_api
EBook
SAMPLE
註釋
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.