Practical Bayesian inference: a primer for physical scientists
"Science is fundamentally about learning from data, and doing so in the presence of uncertainty. Uncertainty arises inevitably and avoidably in many guises. It comes from noise in our measurements: we cannot measure exactly. It comes from sampling effects: we cannot measure everything. It comes...
Gespeichert in:
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| Dokumenttyp: | Buch/Monographie |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge New York Melbourne Delhi Singapore
Cambridge University Press
2017
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| Online-Zugang: | Verlag, Inhaltsverzeichnis, Inhaltsverzeichnis: http://www.gbv.de/dms/tib-ub-hannover/881096814.pdf
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| Verfasserangaben: | Coryn A.L. Bailer-Jones (Max Planck Institute for Astronomy, Heidelberg) |
| Zusammenfassung: | "Science is fundamentally about learning from data, and doing so in the presence of uncertainty. Uncertainty arises inevitably and avoidably in many guises. It comes from noise in our measurements: we cannot measure exactly. It comes from sampling effects: we cannot measure everything. It comes from complexity: data may be numerous, high dimensional, and correlated, making it difficult to see structures. This book is an introduction to statistical methods for analysing data. It presents the major concepts of probability and statistics as well as the computational tools we need to extract meaning from data in the presence of uncertainty"-- |
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| Beschreibung: | Includes bibliographical references (pages 289-209) and index |
| ISBN: | 1107192110 1316642216 9781107192119 9781316642214 |