Advanced Statistical Methods for Astrophysical Probes of Cosmology

Marisa Cristina March

ISBN 10: 3642444547 / ISBN 13: 9783642444548

Editore: Springer Berlin Heidelberg Feb 2015, 2015

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This item is printed on demand - it takes 3-4 days longer - Neuware -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. 200 pp. Englisch.

Codice articolo 9783642444548

Dati bibliografici

• Titolo: Advanced Statistical Methods for Astrophysical Probes of Cosmology
• Editore: Springer Berlin Heidelberg Feb 2015
• Data di pubblicazione: 2015
• Lingua: Inglese
• ISBN 10: 3642444547
• ISBN 13: 9783642444548
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 12 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 12 millimetri di profondità
• Peso: 312 grammi

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

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.