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000944017 019__ $$a1197724062$$a1197948272
000944017 020__ $$a9783030558970$$q(electronic book)
000944017 020__ $$a3030558975$$q(electronic book)
000944017 020__ $$z3030558967
000944017 020__ $$z9783030558963
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000944017 050_4 $$aQA279.5$$b.O35 2020
000944017 08204 $$a519.5/42$$223
000944017 1001_ $$aOijen, Marcel van,$$eauthor.
000944017 24510 $$aBayesian Compendium /$$cMarcel van Oijen.
000944017 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2020]
000944017 300__ $$a1 online resource (XIV, 204 pages 60 illus., 23 illus. in color.)
000944017 336__ $$atext$$btxt$$2rdacontent
000944017 337__ $$acomputer$$bc$$2rdamedia
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000944017 5050_ $$aPreface -- 1 Introduction to Bayesian thinking -- 2 Introduction to Bayesian science -- 3 Assigning a prior distribution -- 4 Assigning a likelihood function -- 5 Deriving the posterior distribution -- 6 Sampling from any distribution by MCMC -- 7 Sampling from the posterior distribution by MCMC -- 8 Twelve ways to fit a straight line -- 9 MCMC and complex models -- 10 Bayesian calibration and MCMC: Frequently asked questions -- 11 After the calibration: Interpretation, reporting, visualization -- 2 Model ensembles: BMC and BMA -- 13 Discrepancy -- 14 Gaussian Processes and model emulation -- 15 Graphical Modelling (GM) -- 16 Bayesian Hierarchical Modelling (BHM) -- 17 Probabilistic risk analysis and Bayesian decision theory -- 18 Approximations to Bayes -- 19 Linear modelling: LM, GLM, GAM and mixed models -- 20 Machine learning -- 21 Time series and data assimilation -- 22 Spatial modelling and scaling error -- 23 Spatio-temporal modelling and adaptive sampling -- 24 What next? -- Appendix 1: Notation and abbreviations -- Appendix 2: Mathematics for modellers -- Appendix 3: Probability theory for modellers -- Appendix 4: R -- Appendix 5: Bayesian software.
000944017 506__ $$aAccess limited to authorized users.
000944017 520__ $$aThis book describes how Bayesian methods work. Its primary aim is to demystify them, and to show readers: Bayesian thinking isnt difficult and can be used in virtually every kind of research. In addition to revealing the underlying simplicity of statistical methods, the book explains how to parameterise and compare models while accounting for uncertainties in data, model parameters and model structures. How exactly should data be used in modelling? The literature offers a bewildering variety of techniques and approaches (Bayesian calibration, data assimilation, Kalman filtering, model-data fusion). This book provides a short and easy guide to all of these and more. It was written from a unifying Bayesian perspective, which reveals how the multitude of techniques and approaches are in fact all related to one another. Basic notions from probability theory are introduced. Executable code examples are included to enhance the books practical use for scientific modellers, and all code is available online as well.
000944017 588__ $$aDescription based on online resource; title from digital title page (viewed on October 29, 2020).
000944017 650_0 $$aBayesian statistical decision theory.
000944017 77608 $$iPrint version: $$z3030558967$$z9783030558963$$w(OCoLC)1176316960
000944017 852__ $$bebk
000944017 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=https://dx.doi.org/10.1007/978-3-030-55897-0$$zOnline Access
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