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...
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| Main Author: | |
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| Format: | Book/Monograph |
| Language: | English |
| Published: |
Cambridge New York Melbourne Delhi Singapore
Cambridge University Press
2017
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| Subjects: | |
| Online Access: | Verlag, Inhaltsverzeichnis, Inhaltsverzeichnis: http://www.gbv.de/dms/tib-ub-hannover/881096814.pdf
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| Author Notes: | Coryn A.L. Bailer-Jones (Max Planck Institute for Astronomy, Heidelberg) |
Table of 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