LEADER 04188nam 22006975 450 001 9910886093703321 005 20260112132443.0 010 $a9783031660856 010 $a3031660854 024 7 $a10.1007/978-3-031-66085-6 035 $a(CKB)34343139900041 035 $a(MiAaPQ)EBC31622015 035 $a(Au-PeEL)EBL31622015 035 $a(DE-He213)978-3-031-66085-6 035 $a(EXLCZ)9934343139900041 100 $a20240828d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBayesian Compendium /$fby Marcel van Oijen 205 $a2nd ed. 2024. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2024. 215 $a1 online resource (261 pages) 311 08$a9783031660849 311 08$a3031660846 327 $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?. 330 $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. 606 $aStatistics 606 $aStatistics 606 $aBiometry 606 $aEcology 606 $aEnvironmental monitoring 606 $aStatistical Theory and Methods 606 $aBayesian Inference 606 $aBiostatistics 606 $aEcology 606 $aEnvironmental Monitoring 606 $aEstadística bayesiana$2thub 608 $aLlibres electrònics$2thub 615 0$aStatistics. 615 0$aStatistics. 615 0$aBiometry. 615 0$aEcology. 615 0$aEnvironmental monitoring. 615 14$aStatistical Theory and Methods. 615 24$aBayesian Inference. 615 24$aBiostatistics. 615 24$aEcology. 615 24$aEnvironmental Monitoring. 615 7$aEstadística bayesiana 676 $a519.542 700 $avan Oijen$b Marcel$01267994 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910886093703321 996 $aBayesian Compendium$94257988 997 $aUNINA