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Bayesian Methods in Cosmology

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John Skilling, D. S. Sivia, Steve Rawlings, Antony Lewis, Sarah Bridle, Andrew R. Liddle, Pia Mukherjee, David Parkinson, Roberto Trotta, Martin Kunz, M. P. Hobson, M. A. J. Ashdown, V. Stolyarov, Graça Rocha, R. Savage, Daniel Mortlock, Neil Cornish, Andrew H. Jaffe, Thomas J. Loredo, Martin A. Hendry, Stefano Andreon, Ofer Lahav, Filipe B. Abdalla, Manda Banerji
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  • format: Paperback
  • isbn: 9781107631755

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  • In recent years cosmologists have advanced from largely qualitative models of the Universe to precision modelling using Bayesian methods, in order to determine the properties of the Universe to high accuracy. This timely book is the only comprehensive introduction to the use of Bayesian methods in cosmological studies, and is an essential reference for graduate students and researchers in cosmology, astrophysics and applied statistics. The first part of the book focuses on methodology, setting the basic foundations and giving a detailed description of techniques. It covers topics including the estimation of parameters, Bayesian model comparison, and separation of signals. The second part explores a diverse range of applications, from the detection of astronomical sources (including through gravitational waves), to cosmic microwave background analysis and the quantification and classification of galaxy properties. Contributions from 24 highly regarded cosmologists and statisticians make this an authoritative guide to the subject.

    • The only comprehensive introduction to Bayesian cosmology, an essential reference for graduate students and researchers
    • Contains contributions from a wide range of experts, including cosmologists and statisticians
    • Describes methods and techniques in detail, and covers wide range of applications
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    Product details

    • format: Paperback
    • isbn: 9781107631755
    • length: 316 pages
    • dimensions: 244 x 170 x 17 mm
    • weight: 0.5kg
  • Table of Contents

    Preface
    Part I. Methods:
    1. Foundations and algorithms John Skilling
    2. Simple applications of Bayesian methods D. S. Sivia and Steve Rawlings
    3. Parameter estimation using Monte Carlo sampling Antony Lewis and Sarah Bridle
    4. Model selection and multi-model interference Andrew R. Liddle, Pia Mukherjee and David Parkinson
    5. Bayesian experimental design and model selection forecasting Roberto Trotta, Martin Kunz, Pia Mukherjee and David Parkinson
    6. Signal separation in cosmology M. P. Hobson, M. A. J. Ashdown and V. Stolyarov
    Part II. Applications:
    7. Bayesian source extraction M. P. Hobson, Graça Rocha and R. Savage
    8. Flux measurement Daniel Mortlock
    9. Gravitational wave astronomy Neil Cornish
    10. Bayesian analysis of cosmic microwave background data Andrew H. Jaffe
    11. Bayesian multilevel modelling of cosmological populations Thomas J. Loredo and Martin A. Hendry
    12. A Bayesian approach to galaxy evolution studies Stefano Andreon
    13. Photometric redshift estimation: methods and applications Ofer Lahav, Filipe B. Abdalla and Manda Banerji
    Index.

  • Resources for

    Bayesian Methods in Cosmology

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  • Editors

    Michael P. Hobson, University of Cambridge
    M. P. Hobson is Reader in Astrophysics and Cosmology at the Cavendish Laboratory, University of Cambridge, where he researches theoretical and observational cosmology, Bayesian statistical methods, gravitation and theoretical optics.

    Andrew H. Jaffe, Imperial College of Science, Technology and Medicine, London
    Andrew H. Jaffe is Professor of Astrophysics and Cosmology at Imperial College and a member of the Planck Surveyor Satellite collaboration, which will create the highest-resolution and most sensitive maps of the CMB ever produced.

    Andrew R. Liddle, University of Sussex
    Andrew R. Liddle is Professor of Astrophysics at the University of Sussex. He is the author of over 150 journal articles and four books on cosmology, covering topics from early Universe theory to modelling astrophysical data.

    Pia Mukherjee, University of Sussex
    Pia Mukherjee is a Postdoctoral Researcher in the Astronomy Centre at the University of Sussex, specialising in constraining cosmological models, including dark energy models, from observational data.

    David Parkinson, University of Sussex
    David Parkinson is a Postdoctoral Research Fellow in the Astronomy Centre at the University of Sussex, working in the areas of cosmology and the early Universe.

    Contributors

    John Skilling, D. S. Sivia, Steve Rawlings, Antony Lewis, Sarah Bridle, Andrew R. Liddle, Pia Mukherjee, David Parkinson, Roberto Trotta, Martin Kunz, M. P. Hobson, M. A. J. Ashdown, V. Stolyarov, Graça Rocha, R. Savage, Daniel Mortlock, Neil Cornish, Andrew H. Jaffe, Thomas J. Loredo, Martin A. Hendry, Stefano Andreon, Ofer Lahav, Filipe B. Abdalla, Manda Banerji

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