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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.
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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