Overview
- Shows how Bayesian algorithms work in an easy to understand way
- Explains Markov Chain Monte Carlo sampling with straightforward examples
- Complemented with the R codes used in the book for modelling, data analysis and visualisation
- Covers complex process-based models as well as simple regression methods and includes chapters on model emulation, graphical modelling, hierarchical modelling, risk analysis and machine learning
- Includes supplementary material: sn.pub/extras
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Table of contents (24 chapters)
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Front Matter
Keywords
About this book
This 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, etc). 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, and all code is available online as well.
Reviews
Authors and Affiliations
About the author
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.
Bibliographic Information
Book Title: Bayesian Compendium
Authors: Marcel van Oijen
DOI: https://doi.org/10.1007/978-3-030-55897-0
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-55896-3Published: 18 September 2020
Softcover ISBN: 978-3-030-55899-4Published: 19 September 2021
eBook ISBN: 978-3-030-55897-0Published: 17 September 2020
Edition Number: 1
Number of Pages: XIV, 204
Number of Illustrations: 37 b/w illustrations, 23 illustrations in colour
Topics: Bayesian Inference, Statistics for Life Sciences, Medicine, Health Sciences, Ecology, Statistical Theory and Methods, Monitoring/Environmental Analysis, Analytical Chemistry