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Bayesian astrophysics/ Andres Asensio Ramos and Inigo Arregui eds.

Contributor(s): Series: Canary Islands Winter School of Astrophysics ; XXVIPublication details: United Kingdom: CUP, 2018Description: xiii, 194 pages, 25 cmISBN:
  • 9781107102132
Subject(s): DDC classification:
  • 23 523.01015 R175
Contents:
Preface -- 1. Bayesian inference and computation: a beginner's guide -- 2. Inverse problems in astronomy -- 3. Bayesian inference in extrasolar planet searches -- 4. Bayesian cosmology -- 5. An Introduction to objective bayesian statistics
Summary: 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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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 523.01015 R175 (Browse shelf(Opens below)) Available 138524
Total holds: 0

Includes bibliographical references

Preface -- 1. Bayesian inference and computation: a beginner's guide -- 2. Inverse problems in astronomy -- 3. Bayesian inference in extrasolar planet searches -- 4. Bayesian cosmology -- 5. An Introduction to objective bayesian statistics

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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