Practical Bayesian inference: a primer for physical scientists
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 th...
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| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2017
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| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 UBM01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (ix, 295 Seiten) |
| ISBN: | 9781108123891 |
| DOI: | 10.1017/9781108123891 |
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Datensatz im Suchindex
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| author | Bailer-Jones, Coryn A. L. |
| author_GND | (DE-588)1126122696 |
| author_facet | Bailer-Jones, Coryn A. L. |
| author_role | aut |
| author_sort | Bailer-Jones, Coryn A. L. |
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| contents | Probability basics -- Estimation and uncertainty -- Statistical models and inference -- Linear models, least squares, and maximum likelihood -- Parameter estimation: single parameter -- Parameter estimation: multiple parameters -- Approximating distributions -- Monte Carlo methods for inference -- Parameter estimation: Markov Chain Monte Carlo -- Frequentist hypothesis testing -- Model comparison -- Dealing with more complicated problems |
| ctrlnum | (ZDB-20-CBO)CR9781108123891 (OCoLC)1005866381 (DE-599)BVBBV044509596 |
| dewey-full | 519.5/42 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/42 |
| dewey-search | 519.5/42 |
| dewey-sort | 3519.5 242 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1017/9781108123891 |
| format | Electronic eBook |
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| isbn | 9781108123891 |
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| spelling | Bailer-Jones, Coryn A. L. Verfasser (DE-588)1126122696 aut Practical Bayesian inference a primer for physical scientists Coryn A.L. Bailer-Jones Cambridge Cambridge University Press 2017 1 Online-Ressource (ix, 295 Seiten) txt rdacontent c rdamedia cr rdacarrier Probability basics -- Estimation and uncertainty -- Statistical models and inference -- Linear models, least squares, and maximum likelihood -- Parameter estimation: single parameter -- Parameter estimation: multiple parameters -- Approximating distributions -- Monte Carlo methods for inference -- Parameter estimation: Markov Chain Monte Carlo -- Frequentist hypothesis testing -- Model comparison -- Dealing with more complicated problems 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 Mathematische Physik Bayesian statistical decision theory Mathematical physics Bayes-Inferenz (DE-588)4648118-7 gnd rswk-swf Bayes-Inferenz (DE-588)4648118-7 s DE-604 Erscheint auch als Druck-Ausgabe, hardback 978-1-107-19211-9 Erscheint auch als Druck-Ausgabe, paperback 978-1-316-64221-4 https://doi.org/10.1017/9781108123891 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Bailer-Jones, Coryn A. L. Practical Bayesian inference a primer for physical scientists Probability basics -- Estimation and uncertainty -- Statistical models and inference -- Linear models, least squares, and maximum likelihood -- Parameter estimation: single parameter -- Parameter estimation: multiple parameters -- Approximating distributions -- Monte Carlo methods for inference -- Parameter estimation: Markov Chain Monte Carlo -- Frequentist hypothesis testing -- Model comparison -- Dealing with more complicated problems Mathematische Physik Bayesian statistical decision theory Mathematical physics Bayes-Inferenz (DE-588)4648118-7 gnd |
| subject_GND | (DE-588)4648118-7 |
| title | Practical Bayesian inference a primer for physical scientists |
| title_auth | Practical Bayesian inference a primer for physical scientists |
| title_exact_search | Practical Bayesian inference a primer for physical scientists |
| title_full | Practical Bayesian inference a primer for physical scientists Coryn A.L. Bailer-Jones |
| title_fullStr | Practical Bayesian inference a primer for physical scientists Coryn A.L. Bailer-Jones |
| title_full_unstemmed | Practical Bayesian inference a primer for physical scientists Coryn A.L. Bailer-Jones |
| title_short | Practical Bayesian inference |
| title_sort | practical bayesian inference a primer for physical scientists |
| title_sub | a primer for physical scientists |
| topic | Mathematische Physik Bayesian statistical decision theory Mathematical physics Bayes-Inferenz (DE-588)4648118-7 gnd |
| topic_facet | Mathematische Physik Bayesian statistical decision theory Mathematical physics Bayes-Inferenz |
| url | https://doi.org/10.1017/9781108123891 |
| work_keys_str_mv | AT bailerjonescorynal practicalbayesianinferenceaprimerforphysicalscientists |