What is included with this book?
List of contributors | p. ix |
Preface | p. xi |
Methods | p. 1 |
Foundations and algorithms | p. 3 |
Rational inference | p. 3 |
Foundations | p. 4 |
Inference | p. 11 |
Algorithms | p. 20 |
Concluding remarks | p. 32 |
Simple applications of Bayesian methods | p. 36 |
Introduction | p. 36 |
Essentials of modern cosmology | p. 37 |
Theorists and pre-processed data | p. 41 |
Experimentalists and raw measurements | p. 49 |
Concluding remarks | p. 54 |
Parameter estimation using Monte Carlo sampling | p. 57 |
Why do sampling? | p. 57 |
How do I get the samples? | p. 59 |
Have I taken enough samples yet? | p. 69 |
What do I do with the samples? | p. 70 |
Conclusions | p. 77 |
Model selection and multi-model inference | p. 79 |
Introduction | p. 79 |
Levels of Bayesian inference | p. 80 |
The Bayesian framework | p. 82 |
Computing the Bayesian evidence | p. 87 |
Interpretational scales | p. 89 |
Applications | p. 90 |
Conclusions | p. 96 |
Bayesian experimental design and model selection forecasting | p. 99 |
Introduction | p. 99 |
Predicting the effectiveness of future experiments | p. 100 |
Experiment optimization for error reduction | p. 106 |
Experiment optimization for model selection | p. 115 |
Predicting the outcome of model selection | p. 120 |
Summary | p. 124 |
Signal separation in cosmology | p. 126 |
Model of the data | p. 127 |
The hidden, visible and data spaces | p. 128 |
Parameterization of the hidden space | p. 129 |
Choice of data space | p. 133 |
Applying Bayes' theorem | p. 137 |
Non-blind signal separation | p. 140 |
(Semi-)blind signal separation | p. 151 |
Applications | p. 165 |
Bayesian source extraction | p. 167 |
Traditional approaches | p. 168 |
The Bayesian approach | p. 170 |
Variable-source-number models | p. 175 |
Fixed-source-number models | p. 178 |
Single-source models | p. 178 |
Conclusions | p. 191 |
Flux measurement | p. 193 |
Introduction | p. 193 |
Photometric measurements | p. 193 |
Classical flux estimation | p. 196 |
The source population | p. 199 |
Bayesian flux inference | p. 201 |
The faintest sources | p. 204 |
Practical flux measurement | p. 209 |
Gravitational wave astronomy | p. 213 |
A new spectrum | p. 213 |
Gravitational wave data analysis | p. 214 |
The Bayesian approach | p. 220 |
Bayesian analysis of cosmic microwave background data | p. 229 |
Introduction | p. 229 |
The CMB as a hierarchical model | p. 231 |
Polarization | p. 240 |
Complications | p. 242 |
Conclusions | p. 243 |
Bayesian multilevel modelling of cosmological populations | p. 245 |
Introduction | p. 245 |
Galaxy distance indicators | p. 247 |
Multilevel models | p. 252 |
Future directions | p. 261 |
A Bayesian approach to galaxy evolution studies | p. 265 |
Discovery space | p. 265 |
Average versus maximum likelihood | p. 266 |
Priors and Malmquist/Eddington bias | p. 268 |
Small samples | p. 270 |
Measuring a width in the presence of a contaminating population | p. 272 |
Fitting a trend in the presence of outliers | p. 275 |
What is the number returned by tests such as x^{2}, KS, etc.? | p. 280 |
Summary | p. 281 |
Photometric redshift estimation: methods and applications | p. 283 |
Introduction | p. 283 |
Template methods | p. 285 |
Bayesian methods and non-colour priors | p. 286 |
Training methods and neural networks | p. 287 |
Errors on photo-z | p. 289 |
Optimal filters | p. 290 |
Comparison of photo-z codes | p. 290 |
The role of spectroscopic datasets | p. 292 |
Synergy with cosmological probes | p. 294 |
Discussion | p. 296 |
Index | p. 299 |
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