Coryn A. L. Bailer-Jones

Practical Bayesian Inference (eBook, PDF)

A Primer for Physical Scientists

• Format: PDF

Produktbeschreibung

Science is fundamentally about learning from data, and doing so in the presence of uncertainty. This volume is an introduction to the major concepts of probability and statistics, and the computational tools for analysing and interpreting data. It describes the Bayesian approach, and explains how this can be used to fit and compare models in a range of problems. Topics covered include regression, parameter estimation, model assessment, and Monte Carlo methods, as well as widely used classical methods such as regularization and hypothesis testing. The emphasis throughout is on the principles, the unifying probabilistic approach, and showing how the methods can be implemented in practice. R code (with explanations) is included and is available online, so readers can reproduce the plots and results for themselves. Aimed primarily at undergraduate and graduate students, these techniques can be applied to a wide range of data analysis problems beyond the scope of this work.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 27. April 2017
• Englisch
• ISBN-13: 9781108129312
• Artikelnr.: 48417293

Autorenporträt

Coryn A. L. Bailer-Jones was educated at the University of Oxford and the University of Cambridge. He has worked on modelling the processing of metals and has done research into the properties of low mass stars and brown dwarfs. He is a senior staff member at the Max-Planck-Institut für Astronomie, Heidelberg, where he leads a group working on the analysis of data from the Gaia survey mission. He also teaches statistics and physics at Universität Heidelberg. His main scientific interests are statistical inference, stars and our Galaxy, and the impact of astronomical phenomena on the Earth.

Inhaltsangabe

Preface
1. Probability basics
2. Estimation and uncertainty
3. Statistical models and inference
4. Linear models, least squares, and maximum likelihood
5. Parameter estimation: single parameter
6. Parameter estimation: multiple parameters
7. Approximating distributions
8. Monte Carlo methods for inference
9. Parameter estimation: Markov chain Monte Carlo
10. Frequentist hypothesis testing
11. Model comparison
12. Dealing with more complicated problems
References
Index.