Bayesian Compendium
This book describes how Bayesian methods work. Aiming to demystify the approach, it explains how to parameterize and compare models while accounting for uncertainties in data, model parameters and model structures. Bayesian thinking is not difficult and can be used in virtually every kind of researc...
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| Format: | Électronique eBook |
| Langue: | anglais |
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Cham :
Springer International Publishing : Imprint: Springer,
2024.
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| Édition: | 2nd ed. 2024. |
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| Accès en ligne: | https://doi.org/10.1007/978-3-031-66085-6 |
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| 100 | 1 | |a van Oijen, Marcel. |e author. |0 (orcid)0000-0003-4028-3626 |1 https://orcid.org/0000-0003-4028-3626 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Bayesian Compendium |h [electronic resource] / |c by Marcel van Oijen. |
| 250 | |a 2nd ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2024. | |
| 300 | |a 1 online resource (XVI, 265 p. 199 illus.). |b 148 illus. in color. | ||
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| 337 | |a computer |b c |2 rdamedia | ||
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| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
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| 505 | 0 | |a - 1. Science and Uncertainty -- 2. Bayesian Inference -- 3. Assigning a Prior Distribution -- 4. Assigning a Likelihood Function -- 5. Deriving the Posterior Distribution -- 6. Markov Chain Monte Carlo Sampling (MCMC) -- 7. Sampling from the Posterior Distribution by MCMC -- 8. MCMC and Multivariate Models -- 9. Bayesian Calibration and MCMC: Frequently Asked Questions -- 10. After the Calibration: Interpretation, Reporting, Visualisation -- 11. Model Ensembles: BMC and BMA -- 12. Discrepancy -- 13. Approximations to Bayes -- 14.Thirteen Ways to Fit a Straight Line -- 15. Gaussian Processes and Model Emulation -- 16. Graphical Modelling -- 17. Bayesian Hierarchical Modelling -- 18. Probabilistic Risk Analysis -- 19. Bayesian Decision Theory -- 20. Linear Modelling: LM, GLM, GAM and Mixed Models -- 21. Machine Learning -- 22. Time Series and Data Assimilation -- 23. Spatial Modelling and Scaling Error -- 24. Spatio-Temporal Modelling and Adaptive Sampling -- 25. What Next?. | |
| 520 | |a This book describes how Bayesian methods work. Aiming to demystify the approach, it explains how to parameterize and compare models while accounting for uncertainties in data, model parameters and model structures. Bayesian thinking is not difficult and can be used in virtually every kind of research. How exactly should data be used in modelling? The literature offers a bewildering variety of techniques (Bayesian calibration, data assimilation, Kalman filtering, model-data fusion, …). This book provides a short and easy guide to all these approaches and more. Written from a unifying Bayesian perspective, it reveals how these methods are related to one another. Basic notions from probability theory are introduced and executable R codes for modelling, data analysis and visualization are included to enhance the book’s practical use. The codes are also freely available online. This thoroughly revised second edition has separate chapters on risk analysis and decision theory. It also features an expanded text on machine learning with an introduction to natural language processing and calibration of neural networks using various datasets (including the famous iris and MNIST). Literature references have been updated and exercises with solutions have doubled in number. | ||
| 532 | 8 | |a Accessibility summary: This PDF does Not fully comply with PDF/UA standards but does include limited screen reader support, descriptions for Non-text content (such as images and graphs), bookmarks for easy navigation, and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognise the importance of accessibility and welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us via https://www.springernature.com/gp/info/accessibility | |
| 532 | 8 | |a Inaccessible, or known limited accessibility | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Statistics . | |
| 650 | 0 | |a Biometry. | |
| 650 | 0 | |a Ecology . | |
| 650 | 0 | |a Environmental monitoring. | |
| 650 | 1 | 4 | |a Statistical Theory and Methods. |
| 650 | 2 | 4 | |a Bayesian Inference. |
| 650 | 2 | 4 | |a Biostatistics. |
| 650 | 2 | 4 | |a Ecology. |
| 650 | 2 | 4 | |a Environmental Monitoring. |
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| 776 | 0 | 8 | |i Printed edition: |z 9783031660849 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031660863 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031660870 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-66085-6 |
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