Advanced statistical methods for astrophysical probes of cosmology Marisa Cristina March

Format:

Book

Thesis/Dissertation

Author/Creator:

March, Marisa Cristina

Series:

Springer theses

Springer theses 2190-5053

Language:

English

Subjects (All):

Astrophysics--Statistical methods.

Astrophysics.

Cosmology.

cosmology.

Genre:

dissertations

Academic theses

Physical Description:

1 online resource

Place of Publication:

Berlin New York Springer ©2013

Language Note:

English

Summary:

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

Contents:

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 SNe Ia Data Robustness to Systematic Error for Future Dark Energy Probes Summary and Conclusions

Notes:

Ph. D. Imperial College, London

Includes bibliographical references and index

ISBN:

9783642350603

3642350607

1299197868

9781299197862

OCLC:

826707599

Access Restriction:

Restricted for use by site license