Bayesian Methods in Cosmology

doi:10.1017/CBO9780511802461

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.

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Contents

Frontmatter
pp i-iv
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Contents
pp v-viii
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List of contributors
pp ix-x
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Preface
pp xi-xii
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Part I - Methods
pp 1-2
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1 - Foundations and algorithms
pp 3-35
- By John Skilling, Maximum Entropy Data Consultants Ltd, Kenmare, County Kerry, Ireland
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2 - Simple applications of Bayesian methods
pp 36-56
- By D. S. Sivia, St John's College, St. Giles, Oxford OX1 3JP, UK, S. G. Rawlings, Astrophysics, Department of Physics, Oxford University, Keble Road, Oxford OX1 3RH, UK
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3 - Parameter estimation using Monte Carlo sampling
pp 57-78
- By Antony Lewis, Institute of Astronomy and Kavli Institute for Cosmology, Madingley Road, Cambridge CB3 0HA, UK, Sarah Bridle, Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK
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4 - Model selection and multi-model inference
pp 79-98
- By Andrew R. Liddle, Astronomy Centre, University of Sussex, Brighton BN1 9QH, UK, Pia Mukherjee, Astronomy Centre, University of Sussex, Brighton BN1 9QH, UK, David Parkinson, Astronomy Centre, University of Sussex, Brighton BN1 9QH, UK
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5 - Bayesian experimental design and model selection forecasting
pp 99-125
- By Roberto Trotta, Astrophysics, Department of Physics, Oxford University, Keble Road, Oxford OX1 3RH, UK; and Astrophysics Group, Imperial College London, Blackett Laboratory, London SW7 2AZ, UK, Martin Kunz, Astronomy Centre, University of Sussex, Brighton BN1 9QH, UK, Pia Mukherjee, Astronomy Centre, University of Sussex, Brighton BN1 9QH, UK, David Parkinson, Astronomy Centre, University of Sussex, Brighton BN1 9QH, UK
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6 - Signal separation in cosmology
pp 126-164
- By M. P. Hobson, Astrophysics Group, Cavendish Laboratory, JJ Thomson Avenue, Cambridge CB3 0HE, UK, M. A. J. Ashdown, Astrophysics Group, Cavendish Laboratory, JJ Thomson Avenue, Cambridge CB3 0HE, UK, V. Stolyarov, Astrophysics Group, Cavendish Laboratory, JJ Thomson Avenue, Cambridge CB3 0HE, UK
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Part II - Applications
pp 165-166
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7 - Bayesian source extraction
pp 167-192
- By M. P. Hobson, Astrophysics Group, Cavendish Laboratory, JJ Thomson Avenue, Cambridge CB3 0HE, UK, Graça Rocha, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125, USA, Richard S. Savage, Astronomy Centre, University of Sussex, Brighton BN1 9QH, UK; Systems Biology Centre, University of Warwick, Coventry CV4 7AL, UK
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8 - Flux measurement
pp 193-212
- By Daniel Mortlock, Astrophysics Group, Imperial College London, Blackett Laboratory, London SW7 2AZ, UK
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9 - Gravitational wave astronomy
pp 213-228
- By Neil Cornish, Department of Physics, Montana State University, Bozeman, MT 597717, USA
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10 - Bayesian analysis of cosmic microwave background data
pp 229-244
- By Andrew H. Jaffe, Astrophysics Group, Imperial College London, Blackett Laboratory, London SW7 2AZ, UK
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11 - Bayesian multilevel modelling of cosmological populations
pp 245-264
- By Thomas J. Loredo, Department of Astronomy, Cornell University, Ithaca, NY 14853, USA, Martin A. Hendry, Department of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, UK
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12 - A Bayesian approach to galaxy evolution studies
pp 265-282
- By Stefano Andreon, INAF–Osservatorio Astronomico di Brera via Brera 28, 20121 Milano, Italy
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13 - Photometric redshift estimation: methods and applications
pp 283-298
- By Ofer Lahav, Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK, Filipe B. Abdalla, Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK, Manda Banerji, Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK
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Index
pp 299-303
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Bayesian Methods in Cosmology

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Contents

Frontmatter
pp i-iv

Contents
pp v-viii

List of contributors
pp ix-x

Preface
pp xi-xii

Part I - Methods
pp 1-2

1 - Foundations and algorithms
pp 3-35

2 - Simple applications of Bayesian methods
pp 36-56

3 - Parameter estimation using Monte Carlo sampling
pp 57-78

4 - Model selection and multi-model inference
pp 79-98

5 - Bayesian experimental design and model selection forecasting
pp 99-125

6 - Signal separation in cosmology
pp 126-164

Part II - Applications
pp 165-166

7 - Bayesian source extraction
pp 167-192

8 - Flux measurement
pp 193-212

9 - Gravitational wave astronomy
pp 213-228

10 - Bayesian analysis of cosmic microwave background data
pp 229-244

11 - Bayesian multilevel modelling of cosmological populations
pp 245-264

12 - A Bayesian approach to galaxy evolution studies
pp 265-282

13 - Photometric redshift estimation: methods and applications
pp 283-298

Index
pp 299-303

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Bayesian Methods in Cosmology

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