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.
"Sinopsis" puede pertenecer a otra edición de este libro.
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.
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
EUR 105,00 gastos de envío desde Alemania a Estados Unidos de America
Destinos, gastos y plazos de envíoEUR 3,43 gastos de envío en Estados Unidos de America
Destinos, gastos y plazos de envíoLibrería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
Condición: New. Nº de ref. del artículo: ABLIING23Mar3113020224837
Cantidad disponible: Más de 20 disponibles
Librería: California Books, Miami, FL, Estados Unidos de America
Condición: New. Nº de ref. del artículo: I-9783642350597
Cantidad disponible: Más de 20 disponibles
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Condición: New. In. Nº de ref. del artículo: ria9783642350597_new
Cantidad disponible: Más de 20 disponibles
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Buch. Condición: 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. Nº de ref. del artículo: 9783642350597
Cantidad disponible: 2 disponibles
Librería: Books Puddle, New York, NY, Estados Unidos de America
Condición: New. pp. 200. Nº de ref. del artículo: 2658578990
Cantidad disponible: 4 disponibles
Librería: Majestic Books, Hounslow, Reino Unido
Condición: New. Print on Demand pp. 200 46 Illus. (11 Col.). Nº de ref. del artículo: 51013617
Cantidad disponible: 4 disponibles
Librería: Biblios, Frankfurt am main, HESSE, Alemania
Condición: New. PRINT ON DEMAND pp. 200. Nº de ref. del artículo: 1858578980
Cantidad disponible: 4 disponibles
Librería: Revaluation Books, Exeter, Reino Unido
Hardcover. Condición: Brand New. 2013 edition. 197 pages. 9.25x6.25x0.50 inches. In Stock. Nº de ref. del artículo: x-3642350593
Cantidad disponible: 2 disponibles
Librería: moluna, Greven, Alemania
Gebunden. Condición: 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. Nº de ref. del artículo: 5057832
Cantidad disponible: Más de 20 disponibles
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Buch. Condición: 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. Nº de ref. del artículo: 9783642350597
Cantidad disponible: 2 disponibles