Practical statistics for astronomers /:
"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 e...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [England] ; New York :
Cambridge University Press,
2012.
|
| Ausgabe: | 2nd ed. |
| Schriftenreihe: | Cambridge observing handbooks for research astronomers ;
8. |
| Schlagworte: | |
| Online-Zugang: | DE-862 DE-863 |
| Zusammenfassung: | "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"-- |
| Beschreibung: | 1 online resource (xix, 353 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781139379380 1139379380 9781139031998 1139031996 9781139376525 1139376527 1139375091 9781139375092 9780521519687 0521519683 |
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| 245 | 1 | 0 | |a Practical statistics for astronomers / |c J.V. Wall, C.R. Jenkins. |
| 250 | |a 2nd ed. | ||
| 260 | |a Cambridge [England] ; |a New York : |b Cambridge University Press, |c 2012. | ||
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| 490 | 1 | |a Cambridge observing handbooks for research astronomers ; |v 8 | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "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"-- |c Provided by publisher | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 650 | 0 | |a Statistical astronomy. |0 http://id.loc.gov/authorities/subjects/sh85127562 | |
| 650 | 6 | |a Statistique stellaire. | |
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| 650 | 7 | |a Statistical astronomy |2 fast | |
| 700 | 1 | |a Jenkins, C. R., |d 1955- |1 https://id.oclc.org/worldcat/entity/E39PCjKQgptTdX6D67RQ8Tv8RX |0 http://id.loc.gov/authorities/names/n2003003717 | |
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| 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. 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. 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. 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. |
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| discipline | Mathematik |
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| illustrated | Illustrated |
| indexdate | 2025-04-11T08:40:56Z |
| institution | BVB |
| isbn | 9781139379380 1139379380 9781139031998 1139031996 9781139376525 1139376527 1139375091 9781139375092 9780521519687 0521519683 |
| language | English |
| oclc_num | 801406073 |
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| owner | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| physical | 1 online resource (xix, 353 pages) : illustrations |
| psigel | ZDB-4-EBA FWS_PDA_EBA ZDB-4-EBA |
| publishDate | 2012 |
| publishDateSearch | 2012 |
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| publisher | Cambridge University Press, |
| record_format | marc |
| series | Cambridge observing handbooks for research astronomers ; |
| series2 | Cambridge observing handbooks for research astronomers ; |
| spelling | Wall, J. V. Practical statistics for astronomers / J.V. Wall, C.R. Jenkins. 2nd ed. Cambridge [England] ; New York : Cambridge University Press, 2012. 1 online resource (xix, 353 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier polychrome rdacc illustration rdaill text file rdaft Cambridge observing handbooks for research astronomers ; 8 Includes bibliographical references and index. "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 Print version record. 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. 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. 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. 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. Statistical astronomy. http://id.loc.gov/authorities/subjects/sh85127562 Statistique stellaire. SCIENCE Astronomy. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Statistical astronomy fast Jenkins, C. R., 1955- https://id.oclc.org/worldcat/entity/E39PCjKQgptTdX6D67RQ8Tv8RX http://id.loc.gov/authorities/names/n2003003717 Print version: Wall, J.V. Practical statistics for astronomers. 2nd ed. Cambridge [England] ; New York : Cambridge University Press, 2012 9780521732499 (DLC) 2012006798 (OCoLC)768071782 Cambridge observing handbooks for research astronomers ; 8. http://id.loc.gov/authorities/names/n98096143 |
| spellingShingle | Wall, J. V. Practical statistics for astronomers / Cambridge observing handbooks for research astronomers ; 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. 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. 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. 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. Statistical astronomy. http://id.loc.gov/authorities/subjects/sh85127562 Statistique stellaire. SCIENCE Astronomy. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Statistical astronomy fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85127562 |
| title | Practical statistics for astronomers / |
| title_auth | Practical statistics for astronomers / |
| title_exact_search | Practical statistics for astronomers / |
| title_full | Practical statistics for astronomers / J.V. Wall, C.R. Jenkins. |
| title_fullStr | Practical statistics for astronomers / J.V. Wall, C.R. Jenkins. |
| title_full_unstemmed | Practical statistics for astronomers / J.V. Wall, C.R. Jenkins. |
| title_short | Practical statistics for astronomers / |
| title_sort | practical statistics for astronomers |
| topic | Statistical astronomy. http://id.loc.gov/authorities/subjects/sh85127562 Statistique stellaire. SCIENCE Astronomy. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Statistical astronomy fast |
| topic_facet | Statistical astronomy. Statistique stellaire. SCIENCE Astronomy. MATHEMATICS Probability & Statistics General. Statistical astronomy |
| work_keys_str_mv | AT walljv practicalstatisticsforastronomers AT jenkinscr practicalstatisticsforastronomers |