Practical Statistics for Astronomers
Astronomy needs statistical methods to interpret data, but statistics is a many-faceted subject that 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 simulations, 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, solutions to problems, and other resources 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.
1103364052
Practical Statistics for Astronomers
Astronomy needs statistical methods to interpret data, but statistics is a many-faceted subject that 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 simulations, 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, solutions to problems, and other resources 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.
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Practical Statistics for Astronomers

Practical Statistics for Astronomers

Practical Statistics for Astronomers

Practical Statistics for Astronomers

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Overview

Astronomy needs statistical methods to interpret data, but statistics is a many-faceted subject that 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 simulations, 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, solutions to problems, and other resources 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.

Product Details

ISBN-13: 9780521732499
Publisher: Cambridge University Press
Publication date: 04/26/2012
Series: Cambridge Observing Handbooks for Research Astronomers , #8
Edition description: New Edition
Pages: 374
Product dimensions: 6.00(w) x 8.90(h) x 0.80(d)

About the Author

J. V. Wall is Adjunct Professor in the Department of Physics and Astronomy, University of British Columbia and Visiting Professor at the University of Oxford.

C. R. Jenkins is a Research Scientist in Earth Sciences and Resource Engineering at the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia.

Table of Contents

1. Decision; 2. Probability; 3. Statistics and expectations; 4. Correlation and association; 5. Hypothesis-testing; 6. Data modelling and parameter-estimation: basics; 7. Data modelling and parameter-estimation: advanced topics; 8. Detection and surveys; 9. Sequential data - 1D statistics; 10. Statistics of large-scale structure; 11. Epilogue: statistics and our Universe; Appendices; References; Index.
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