Bayesian Compendium

Author:   Marcel van Oijen
Publisher:   Springer Nature Switzerland AG
Edition:   2020 ed.
ISBN:  

9783030558994


Pages:   204
Publication Date:   19 September 2021
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Bayesian Compendium


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Author:   Marcel van Oijen
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   2020 ed.
Weight:   0.343kg
ISBN:  

9783030558994


ISBN 10:   3030558991
Pages:   204
Publication Date:   19 September 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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.

Reviews

The writing is succinct and easy to understand. ... The book does cover a wide range of topics in Bayesian science, and that is indeed one of its best features. I do see it serving as a starting point for most non statistically minded researchers, who can get a basic idea about their topic of interest from consulting the book, and then consult references provided to get a more in-depth knowledge. Overall, I do congratulate the author on writing this book. (Sayan Dasgupta, Biometrics, Vol. 78 (2), July, 2022)


Author Information

Marcel van Oijen studied Mathematical Biology at the University of Utrecht and completed his PhD in Plant Disease Epidemiology at Wageningen University, where he subsequently worked on modelling the impacts of environmental change on crops. He is currently a Senior Scientist at the UK’s Natural Environment Research Council, focusing on the use of Bayesian methods in the modelling of ecosystem services provided by grasslands, forests and agroforestry systems.

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