Bayesian compendium /

Bayesian compendium /

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Bibliographic Details
Author / Creator:	Oijen, Marcel van, author.
Imprint:	Cham, Switzerland : Springer, [2020]
Description:	1 online resource (xiv, 204 p.) : ill. (some col.)
Language:	English
Subject:	Bayesian statistical decision theory. Bayesian statistical decision theory. Electronic books.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/12607725

Hidden Bibliographic Details
ISBN:	9783030558970 3030558975 3030558967 9783030558963
Notes:	Description based on online resource; title from digital title page (viewed on October 29, 2020).
Summary:	This 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.
Other form:	Original 3030558967 9783030558963

Table of Contents:

Preface
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.