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Advanced Statistical Methods for Astrophysical Probes of Cosmology

  • Marisa Cristina¬†March

Part of the Springer Theses book series (Springer Theses)

Table of contents

  1. Front Matter
    Pages i-xx
  2. Marisa Cristina March
    Pages 1-5
  3. Marisa Cristina March
    Pages 7-35
  4. Marisa Cristina March
    Pages 37-44
  5. Marisa Cristina March
    Pages 45-55
  6. Marisa Cristina March
    Pages 57-74
  7. Marisa Cristina March
    Pages 95-148
  8. Marisa Cristina March
    Pages 173-174
  9. Back Matter
    Pages 175-177

About this book

Introduction

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.

Keywords

Baryon Acoustic Oscillations Bayesian Hierarchical Model Bayesian Model Selection Bayesian Statistics Cosmological Model Selection Cosmological Parameters Dark Energy Parameters Expansion of the Universe Fisher Forecasts Statistical Models for Observational Cosmology Supernovae Type Ia

Authors and affiliations

  • Marisa Cristina¬†March
    • 1
  1. 1., University of SussexAstronomy CentreBrightonUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-35060-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Physics and Astronomy
  • Print ISBN 978-3-642-35059-7
  • Online ISBN 978-3-642-35060-3
  • Series Print ISSN 2190-5053
  • Series Online ISSN 2190-5061
  • Buy this book on publisher's site