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 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.
Nominated by the astrophysics group of Imperial College, London as best dissertation of 2011 The work presented in this thesis constitutes a major leap forward in the field of supernova cosmology Opens the way to more accurate and robust constraints on dark energy properties Stands out for the sophistication of the statistical approach adopted