
Title  Advanced statistical methods for astrophysical probes of cosmology 
Author(s)  Marisa Cristina March 
Publication  Berlin, Springer, 2013. 
Description  1 online resource 
Abstract Note  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 
Notes  "Doctoral thesis accepted by the Astrophysics Group of Imperial College London."t.p. Includes bibliographical references and index 
Keyword(s)  1. ASTROPHYSICS
2. COSMOLOGY
3. EBOOK
4. EBOOK  SPRINGER
5. SCIENCE / Physics / Astrophysics

Item Type  eBook 
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Call#  Status  Issued To  Return Due On  Physical Location 
I02155 


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