This book explores advanced Bayesian statistical methods for extracting key information for cosmological model selection, parameter inference and forecasting from astrophysical observations, in particular dark energy from observations of 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 Includes supplementary material: sn.pub/extras

Autorentext
Marisa Cristina March is currently a Postdoctoral Research Fellow at the Univeristy of Sussex, and was formerly a postgraduate cosmology student at Imperial College working with Dr Roberto Trotta, in the field of dark energy science.

Inhalt

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.