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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| Format: | eBook |
| Language: | English |
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Cambridge
Cambridge University Press
2017
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| Collection: | Cambridge Books Online - Collection details see MPG.ReNa |
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