Ebook: 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.

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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.

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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.

Content:
Front Matter....Pages i-xx
Introduction....Pages 1-5
Cosmology Background....Pages 7-35
Dark Energy and Apparent Late Time Acceleration....Pages 37-44
Supernovae Ia....Pages 45-55
Statistical Techniques....Pages 57-74
Bayesian Doubt: Should We Doubt the Cosmological Constant?....Pages 75-93
Bayesian Parameter Inference for SNe Ia Data....Pages 95-148
Robustness to Systematic Error for Future Dark Energy Probes....Pages 149-172
Summary and Conclusions....Pages 173-174
Back Matter....Pages 175-177

---

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.

Content:
Front Matter....Pages i-xx
Introduction....Pages 1-5
Cosmology Background....Pages 7-35
Dark Energy and Apparent Late Time Acceleration....Pages 37-44
Supernovae Ia....Pages 45-55
Statistical Techniques....Pages 57-74
Bayesian Doubt: Should We Doubt the Cosmological Constant?....Pages 75-93
Bayesian Parameter Inference for SNe Ia Data....Pages 95-148
Robustness to Systematic Error for Future Dark Energy Probes....Pages 149-172
Summary and Conclusions....Pages 173-174
Back Matter....Pages 175-177
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