| Edition |
2nd ed. |
| Description |
1 online resource (xix, 353 pages) : illustrations. |
|
data file |
| Physical Medium |
polychrome |
| Series |
Cambridge observing handbooks for research astronomers ; 8
|
|
Cambridge observing handbooks for research astronomers ; 8.
|
| Bibliography |
Includes bibliographical references and index. |
| Summary |
"Astronomy needs statistical methods to interpret data, but statistics is a many-faceted subject which is difficult for non-specialists to access. This handbook helps astronomers analyze the complex data and models of modern astronomy. This second edition has been revised to feature many more examples using Monte Carlo simulation, and now also includes Bayesian inference, Bayes factors and Markov Chain Monte Carlo integration. Chapters cover basic probability, correlation analysis, hypothesis testing, Bayesian modelling, time series analysis, luminosity functions and clustering. Exercises at the end of each chapter guide readers through the techniques and tests necessary for most observational investigations. The data tables from the book are available online at www.cambridge.org/9780521732499. Bringing together the most relevant statistical and probabilistic techniques for use in observational astronomy, this handbook is a practical manual for advanced undergraduate and graduate students and professional astronomers"-- Provided by publisher. |
| Contents |
Cover; Practical Statistics for Astronomers, Second Edition; Cover insets; Cambridge Observing Handbooks for Research Astronomers; Title; Copyright; Contents; Foreword to first edition; Foreword to second edition; Note on notation; 1 Decision; 1.1 How is science done?; 1.2 Probability; probability distributions; 1.3 Bolt-on statistics?; 1.4 Probability and statistics in inference: an overview of this book; 1.5 How to use this book; Exercises; 2 Probability; 2.1 What is probability?; 2.2 Conditionality and independence; 2.3 ... and Bayes' theorem; 2.4 Probability distributions; 2.4.1 Concept. |
|
2.4.2 Some common distributions2.5 Bayesian inferences with probability; 2.6 Monte Carlo generators; Exercises; 3 Statistics and expectations; 3.1 Statistics; 3.2 What should we expect of our statistics?; 3.3 Simple error analysis; 3.3.1 Random or systematic?; 3.3.2 Error propagation; 3.3.3 Combining distributions; 3.4 Some useful statistics, and their distributions; 3.5 Uses of statistics; Exercises; 4 Correlation and association; 4.1 The fishing trip; 4.2 Testing for correlation; 4.2.1 Bayesian correlation-testing; 4.2.2 The classical approach to correlation-testing. |
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4.2.3 Correlation-testing: classical, non-parametric4.2.4 Correlation-testing: Bayesian versus non-Bayesian tests; 4.3 Partial correlation; 4.4 But what next?; 4.5 Principal component analysis; Exercises; 5 Hypothesis testing; 5.1 Methodology of classical hypothesis testing; 5.2 Parametric tests: means and variances, t and F tests; 5.2.1 The Behrens-Fisher Test; 5.2.2 Non-Gaussian parametric testing; 5.2.3 Which model is better? The Bayes factor; 5.3 Non-parametric tests: single samples; 5.3.1 Chi-square test; 5.3.2 Kolmogorov-Smirnov one-sample test; 5.3.3 One-sample runs test of randomness. |
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5.4 Non-parametric tests: two independent samples5.4.1 Fisher exact test; 5.4.2 Chi-square two-sample (or k-sample) test; 5.4.3 Wilcoxon-Mann-Whitney U test; 5.4.4 Kolmogorov-Smirnov two-sample test; 5.5 Summary, one- and two-sample non-parametric tests; 5.6 Statistical ritual; Exercises; 6 Data modelling and parameter estimation: basics; 6.1 The maximum-likelihood method; 6.2 The method of least squares: regression analysis; 6.3 The minimum chi-square method; 6.4 Weighting combinations of data; 6.5 Bayesian likelihood analysis; 6.6 Bootstrap and jackknife; Exercises. |
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7 Data modelling and parameter estimation: advanced topics7.1 Model choice and Bayesian evidence; 7.2 Model simplicity and the Ockham factor; 7.3 The integration problem; 7.4 Pitfalls in model choice; 7.5 The Akaike and Bayesian information criteria; 7.6 Monte Carlo integration: doing the Bayesian integrals; 7.7 The Metropolis-Hastings algorithm; 7.8 Computation of the evidence by MCMC; 7.9 Models of models, and the combination of data sets; 7.10 Broadening the range of models, and weights; 7.11 Press and Kochanek's method; 7.12 Median statistics; Exercises; 8 Detection and surveys. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Statistical astronomy.
|
|
Statistical astronomy. |
| Genre/Form |
Electronic books.
|
|
Electronic books.
|
| Added Author |
Jenkins, C. R., 1955-
|
| Other Form: |
Print version: Wall, J.V. Practical statistics for astronomers. 2nd ed. Cambridge [England] ; New York : Cambridge University Press, 2012 9780521732499 (DLC) 2012006798 (OCoLC)768071782 |
| ISBN |
9781139379380 (electronic book) |
|
1139379380 (electronic book) |
|
9781139031998 (electronic book) |
|
1139031996 (electronic book) |
|
9781139376525 |
|
1139376527 |
|
1139375091 |
|
9781139375092 |
|
9780521732499 |
|
0521732492 |
|
9781139375092 |
| Standard No. |
9786613720269 |
|
