LEADER 00805nam0-22002891i-450- 001 990001603180403321 005 20050119115739.0 035 $a000160318 035 $aFED01000160318 035 $a(Aleph)000160318FED01 035 $a000160318 100 $a20030910d1914----km-y0itay50------ba 101 0 $aita 200 1 $a<>Umbria agricola, industriale, commerciale$fFernando Mancini 210 $aFoligno$cTip. Salvati$d1914 215 $a415 p.$d23 cm 610 0 $aStoria economica 610 0 $aUmbria 676 $a330.9 700 1$aMancini,$bFernando$068071 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001603180403321 952 $a60 330.9 B 80$b20662$fFAGBC 959 $aFAGBC 996 $aUmbria agricola, industriale, commerciale$9368210 997 $aUNINA LEADER 04332nam 22007575 450 001 9910484110903321 005 20250610110431.0 010 $a9783030558970 010 $a3030558975 024 7 $a10.1007/978-3-030-55897-0 035 $a(CKB)4100000011457727 035 $a(DE-He213)978-3-030-55897-0 035 $a(MiAaPQ)EBC6352974 035 $a(PPN)250223503 035 $a(MiAaPQ)EBC6352934 035 $a(MiAaPQ)EBC29228830 035 $a(EXLCZ)994100000011457727 100 $a20200917d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBayesian Compendium /$fby Marcel van Oijen 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XIV, 204 p. 60 illus., 23 illus. in color.) 311 08$a9783030558963 311 08$a3030558967 320 $aIncludes bibliographical references and index. 327 $aPreface -- 1 Introduction to Bayesian thinking -- 2 Introduction to Bayesian science -- 3 Assigning a prior distribution -- 4 Assigning a likelihood function -- 5 Deriving the posterior distribution -- 6 Sampling from any distribution by MCMC -- 7 Sampling from the posterior distribution by MCMC -- 8 Twelve ways to fit a straight line -- 9 MCMC and complex models -- 10 Bayesian calibration and MCMC: Frequently asked questions -- 11 After the calibration: Interpretation, reporting, visualization -- 2 Model ensembles: BMC and BMA -- 13 Discrepancy -- 14 Gaussian Processes and model emulation -- 15 Graphical Modelling (GM) -- 16 Bayesian Hierarchical Modelling (BHM) -- 17 Probabilistic risk analysis and Bayesian decision theory -- 18 Approximations to Bayes -- 19 Linear modelling: LM, GLM, GAM and mixed models -- 20 Machine learning -- 21 Time series and data assimilation -- 22 Spatial modelling and scaling error -- 23 Spatio-temporal modelling and adaptive sampling -- 24 What next? -- Appendix 1: Notation and abbreviations -- Appendix 2: Mathematics for modellers -- Appendix 3: Probability theory for modellers -- Appendix 4: R -- Appendix 5: Bayesian software. 330 $aThis book describes how Bayesian methods work. Its primary aim is to demystify them, and to show readers: Bayesian thinking isn?t difficult and can be used in virtually every kind of research. In addition to revealing the underlying simplicity of statistical methods, the book explains how to parameterise and compare models while accounting for uncertainties in data, model parameters and model structures. How exactly should data be used in modelling? The literature offers a bewildering variety of techniques and approaches (Bayesian calibration, data assimilation, Kalman filtering, model-data fusion). This book provides a short and easy guide to all of these and more. It was written from a unifying Bayesian perspective, which reveals how the multitude of techniques and approaches are in fact all related to one another. Basic notions from probability theory are introduced. Executable code examples are included to enhance the book?s practical use for scientific modellers, andall code is available online as well. 606 $aStatistics 606 $aBiometry 606 $aEcology 606 $aStatistics 606 $aEnvironmental monitoring 606 $aAnalytical chemistry 606 $aBayesian Inference 606 $aBiostatistics 606 $aEcology 606 $aStatistical Theory and Methods 606 $aEnvironmental Monitoring 606 $aAnalytical Chemistry 615 0$aStatistics. 615 0$aBiometry. 615 0$aEcology. 615 0$aStatistics. 615 0$aEnvironmental monitoring. 615 0$aAnalytical chemistry. 615 14$aBayesian Inference. 615 24$aBiostatistics. 615 24$aEcology. 615 24$aStatistical Theory and Methods. 615 24$aEnvironmental Monitoring. 615 24$aAnalytical Chemistry. 676 $a519.542 676 $a519.542 700 $aOijen$b Marcel van$01016394 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484110903321 996 $aBayesian compendium$92377961 997 $aUNINA