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020 _a9783031660856
_9978-3-031-66085-6
024 7 _a10.1007/978-3-031-66085-6
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aPBT
_2thema
100 1 _avan Oijen, Marcel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983040
245 1 0 _aBayesian Compendium
_h[electronic resource] /
_cby Marcel van Oijen.
250 _a2nd ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXVI, 265 p. 199 illus., 148 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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 _aThis 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.
650 0 _aStatistics.
_965982
650 0 _aStatistics .
_965982
650 0 _aBiometry.
_965981
650 0 _aEcology .
_968629
650 0 _aEnvironmental monitoring.
650 1 4 _aStatistical Theory and Methods.
650 2 4 _aBayesian Inference.
_976436
650 2 4 _aBiostatistics.
_965985
650 2 4 _aEcology.
_968629
650 2 4 _aEnvironmental Monitoring.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031660849
776 0 8 _iPrinted edition:
_z9783031660863
776 0 8 _iPrinted edition:
_z9783031660870
856 4 0 _uhttps://doi.org/10.1007/978-3-031-66085-6
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
999 _c62801
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