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Advanced Statistical Methods for Astrophysical Probes of Cosmology: Doctoral Thesis Accepted by the Astrophysics Group of Imperial College London (Springer Theses) - Hardcover
Synopsis
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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About the Author
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.
From the Back Cover
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.
"About this title" may belong to another edition of this title.
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Advanced Statistical Methods for Astrophysical Probes of Cosmology (Springer Theses)
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Advanced Statistical Methods for Astrophysical Probes of Cosmology
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Buch. Condition: Neu. 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. Seller Inventory # 9783642350597
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Advanced Statistical Methods for Astrophysical Probes of Cosmology: Doctoral Thesis Accepted by the Astrophysics Group of Imperial College London
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Advanced Statistical Methods for Astrophysical Probes of Cosmology
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Nominated by the astrophysics group of Imperial College, London as best dissertation of 2011 The work presented in this thesis constitutes a major leap forward in the field of supernova cosmology Opens the way to more accurate and robust con. Seller Inventory # 5057832
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Advanced Statistical Methods for Astrophysical Probes of Cosmology
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Buch. Condition: Neu. 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 200 pp. Englisch. Seller Inventory # 9783642350597
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