Bayesian methods in cosmology:
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
New York
Cambridge Univ. Press
2010
|
| Edition: | 1. publ. |
| Subjects: | |
| Online Access: | Cover image Inhaltsverzeichnis Klappentext |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | XII, 303 S. graph. Darst. |
| ISBN: | 9780521887946 9781107631755 |
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MARC
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Record in the Search Index
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| adam_text | Contents
List of contributors page
ix
Preface
xi
Part I Methods
1
1
Foundations and algorithms
3
John Stilling
1.
1 Rational inference
3
1.2
Foundations
4
1.3
Inference
11
1.4
Algorithms
20
1.5
Concluding remarks
32
2
Simple applications of Bayesian methods
36
D. S. Sivia and S. G. Rawlings
2.1
Introduction
36
2.2
Essentials of modern cosmology
37
2.3
Theorists and pre-processed data
41
2.4
Experimentalists and raw measurements
49
2.5
Concluding remarks
54
3
Parameter estimation using Monte Carlo sampling
57
Antony Lewis and Sarah Bridle
3.1
Why do sampling?
57
3.2
How do I get the samples?
59
3.3
Have I taken enough samples yet?
69
3.4
What do I do with the samples?
70
3.5
Conclusions
77
vi
Contents
4
Model selection and multi-model inference
79
Andrew R. Uddle,
Pia Mukherjee
and David Parkinson
4.1
Introduction
79
4.2
Levels of Bayesian inference
80
4.3
The Bayesian framework
82
4.4
Computing the Bayesian evidence
87
4.5
Interpretational scales
89
4.6
Applications
90
4.7
Conclusions
96
Bayesian experimental design and model selection forecasting
99
Roberto
Trotta,
Martin
Kunz, Pia Mukherjee
and David Parkinson
5.1
Introduction
99
5.2
Predicting the effectiveness of future experiments
100
5.3
Experiment optimization for error reduction
106
5.4
Experiment optimization for model selection
115
5.5
Predicting the outcome of model selection
120
5.6
Summary
124
Signal separation in cosmology
126
M.
Ρ
Hobsony
M. A. J.
Ashdown and V. Stolyarov
6.1
Model
of the data
127
6.2
The hidden, visible and data spaces
128
6.3
Parameterization of the hidden space
129
6.4
Choice of data space
133
6.5
Applying
Bayes
theorem
137
6.6
Non-blind signal separation
140
6.7
(Semi-)blind signal separation
151
Parti
I Applications
165
Bayesian source extraction
167
M.
P. Hobson,
Graça
Rocha
and Richard S. Savage
7.1
Traditional approaches
168
7.2
The Bayesian approach
170
7.3
Variable-source-number models
175
7.4
Fixed-source-number models
178
7.5
Single-source models
178
7.6
Conclusions
191
Contents
vii
8
Flux measurement
193
Daniel
Mortlock
8.1
Introduction
193
8.2
Photometric measurements
193
8.3
Classical flux estimation
196
8.4
The source population
199
8.5
Bayesian flux inference
201
8.6
The faintest sources
204
8.7
Practical flux measurement
209
9
Gravitational wave astronomy
213
Neil Cornish
9.1
A new spectrum
213
9.2
Gravitational wave data analysis
214
9.3
The Bayesian approach
220
10
Bayesian analysis of cosmic microwave background data
229
Andrew H. Jaffe
10.1
Introduction
229
10.2
The CMB as a hierarchical model
231
10.3
Polarization
240
10.4
Complications
242
10.5
Conclusions
243
11
Bayesian multilevel modelling of cosmological populations
245
Thomas J. Loredo and Martin A Hendry
11.1
Introduction
245
11.2
Galaxy distance indicators
247
11.3
Multilevel models
252
11.4
Future directions
261
12
A Bayesian approach to galaxy evolution studies
265
Stefano Andreon
12.1
Discovery space
265
12.2
Average versus maximum likelihood
266
12.3
Priors and Malmquist/Eddington bias
268
12.4
Small samples
270
12.5
Measuring a width in the presence of a contaminating population
272
12.6
Fitting a trend in the presence of outliers
275
12.7
What is the number returned by tests such as
χ2,
KS,
etc.?
280
12.8
Summary
281
viii Contents
13
Photometric redshift estimation: methods and applications
283
Ofer
Lahav,
Filipe
В.
Abdalla and
Manda
Banerji
13.1
Introduction
283
13.2
Template methods
285
13.3
Bayesian methods and non-colour priors
286
13.4
Training methods and neural networks
287
13.5
Errors on photo-z
289
13.6
Optimal filters
290
13.7
Comparison of photo-z codes
290
13.8
The role of
spectroscopie
datasets
292
13.9
Synergy with
cosmologica!
probes
294
13.10
Discussion
296
index
299
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.
Michael 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,
tation and theoretical optics.
Andrew H. Jaffe is Professor of Astrophysics and Cosmology at Imperial College. London, and a member
of the Planck Surveyor Satellite collaboration, which will create the highest-resolution and most sensitive
maps of the cosmic microwave background ever produced.
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 is a Postdoctoral Research Fellow
m
the Astronomy Centre at the University of Sussex,
specializing in constraining cosmological models, including dark energy models, from observational data.
David Parkinson is a Postdoctoral Research Fellow in the Astronomy Centre at the University of Sussex,
-
<ing
m
the areas of cosmology and the early Universe.
ISBN
978-1-107-63175-5
9 781107 631 755
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| spelling | Bayesian methods in cosmology [ed. by] Michael P. Hobson ... [et al.] 1. publ. New York Cambridge Univ. Press 2010 XII, 303 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Cosmology Statistical methods Bayesian statistical decision theory Kosmologie (DE-588)4114294-9 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Kosmologie (DE-588)4114294-9 s Bayes-Entscheidungstheorie (DE-588)4144220-9 s DE-604 Hobson, Michael P. 1967- Sonstige (DE-588)141007885 oth http://assets.cambridge.org/97805218/87946/cover/9780521887946.jpg Cover image Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020332491&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020332491&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Bayesian methods in cosmology Cosmology Statistical methods Bayesian statistical decision theory Kosmologie (DE-588)4114294-9 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| subject_GND | (DE-588)4114294-9 (DE-588)4144220-9 |
| title | Bayesian methods in cosmology |
| title_auth | Bayesian methods in cosmology |
| title_exact_search | Bayesian methods in cosmology |
| title_full | Bayesian methods in cosmology [ed. by] Michael P. Hobson ... [et al.] |
| title_fullStr | Bayesian methods in cosmology [ed. by] Michael P. Hobson ... [et al.] |
| title_full_unstemmed | Bayesian methods in cosmology [ed. by] Michael P. Hobson ... [et al.] |
| title_short | Bayesian methods in cosmology |
| title_sort | bayesian methods in cosmology |
| topic | Cosmology Statistical methods Bayesian statistical decision theory Kosmologie (DE-588)4114294-9 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| topic_facet | Cosmology Statistical methods Bayesian statistical decision theory Kosmologie Bayes-Entscheidungstheorie |
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