Practical Bayesian inference : a primer for physical scientists / Coryn A.L. Bailer-Jones.
Material type:
TextPublication details: Cambridge : Cambridge University Press, 2017.Description: ix, 295 pages : illustrations ; 26 cmISBN: - 9781107192119
- 000SA.161 23 B154
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.161 B154 (Browse shelf(Opens below)) | Available | 138110 |
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| 000SA.16 X8 Intuitionistic fuzzy information aggregation : | 000SA.16 X8 Intuitionistic preference modeling and interactive decision making / | 000SA.16 Z12 Statistical decision problems : | 000SA.161 B154 Practical Bayesian inference : | 000SA.161 B361 Bayesian statistics from methods to models and applications : | 000SA.161 B559 Bayesian programming / | 000SA.161 B865 Bayesian methods for measures of agreement / |
Includes bibliographical references and index.
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.
"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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