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Bayesian Astrophysics

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Title: Bayesian Astrophysics
Description: Bayesian methods are being increasingly employed in many different areas of research in the physical sciences. In astrophysics, models are used to make predictions to be compared to observations. These observations offer information that is incomplete and uncertain, so the comparison has to be pursued by following a probabilistic approach. With contributions from leading experts, this volume covers the foundations of Bayesian inference, a description of computational methods, and recent results from their application to areas such as exoplanet detection and characterisation, image reconstruction, and cosmology. It appeals to both young researchers seeking to learn about Bayesian methods as well as to astronomers wishing to incorporate these approaches in their research areas. It provides the next generation of researchers with the tools of modern data analysis that are already becoming standard in current astrophysical research.
Authors: Andrés Asensio Ramos, Íñigo Arregui
Resource Type: eBook.
Subjects: Bayesian statistical decision theory--Congresses, Astrophysics--Mathematical models--Congresses, Cosmology--Mathematical models--Congresses, Astronomy--Mathematical models--Congresses
Categories: SCIENCE / Physics / Astrophysics
Database: eBook Collection (EBSCOhost)
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  – Type: ebook-pdf
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  Availability: 0
Header DbId: nlebk
DbLabel: eBook Collection (EBSCOhost)
An: 1761506
RelevancyScore: 1084
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 1083.55249023438
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  Data: Bayesian Astrophysics
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  Data: Bayesian methods are being increasingly employed in many different areas of research in the physical sciences. In astrophysics, models are used to make predictions to be compared to observations. These observations offer information that is incomplete and uncertain, so the comparison has to be pursued by following a probabilistic approach. With contributions from leading experts, this volume covers the foundations of Bayesian inference, a description of computational methods, and recent results from their application to areas such as exoplanet detection and characterisation, image reconstruction, and cosmology. It appeals to both young researchers seeking to learn about Bayesian methods as well as to astronomers wishing to incorporate these approaches in their research areas. It provides the next generation of researchers with the tools of modern data analysis that are already becoming standard in current astrophysical research.
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  Data: <searchLink fieldCode="AR" term="%22Andrés+Asensio+Ramos%22">Andrés Asensio Ramos</searchLink><br /><searchLink fieldCode="AR" term="%22Íñigo+Arregui%22">Íñigo Arregui</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Bayesian+statistical+decision+theory--Congresses%22">Bayesian statistical decision theory--Congresses</searchLink><br /><searchLink fieldCode="DE" term="%22Astrophysics--Mathematical+models--Congresses%22">Astrophysics--Mathematical models--Congresses</searchLink><br /><searchLink fieldCode="DE" term="%22Cosmology--Mathematical+models--Congresses%22">Cosmology--Mathematical models--Congresses</searchLink><br /><searchLink fieldCode="DE" term="%22Astronomy--Mathematical+models--Congresses%22">Astronomy--Mathematical models--Congresses</searchLink>
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    Classifications:
      – Code: 523.0101519542
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Bayesian statistical decision theory--Congresses
        Type: general
      – SubjectFull: Astrophysics--Mathematical models--Congresses
        Type: general
      – SubjectFull: Cosmology--Mathematical models--Congresses
        Type: general
      – SubjectFull: Astronomy--Mathematical models--Congresses
        Type: general
    Titles:
      – TitleFull: Bayesian Astrophysics
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Andrés Asensio Ramos
      – PersonEntity:
          Name:
            NameFull: Íñigo Arregui
      – PersonEntity:
          Name:
            NameFull: Andrés Asensio Ramos
      – PersonEntity:
          Name:
            NameFull: Íñigo Arregui
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2018
            – D: 02
              M: 05
              Type: profile
              Y: 2018
          Identifiers:
            – Type: isbn-print
              Value: 9781107102132
            – Type: isbn-electronic
              Value: 9781108619837
          Numbering:
            – Type: volume
              Value: volume XXVI
          Titles:
            – TitleFull: Bayesian Astrophysics
              Type: main
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