This book is intended to reach the broad audience of students and scientists with an intermediate level in maths, that are interested in quantitative analysis of environmental data (they collect field data and/or they want to apply some of their own biological knowledge to interpret such data) but are reluctant to apply ready-made technical recipes without understanding how and why it works. We bet that such people may be willing to take some time to buy and read our book. We would therefore not focus necessarily to up-to-date ecological issues (biodiversity, community behavior, genomics, etc.) but we will show how they could be revisited by using Bayesian modeling techniques. Scientists from other fields than ecology and environment may also get interested in the book since we present examples from the most common statistical models, ranging from simple ones to hierarchical structures. The emphasis on practice is a strong feature of this book in that its primary audience is made of graduate students that need to use Bayesian statistics as a tool to analyse their experiments and/or datasets. We have put much weight on practicals ( 50% within each chapter) so that the interested reader will be ready and able, in practice, to deal with such advanced ecological issues and to implement models of his own.
This book also provides a self-contained Applied Bayesian Statistics course of 12 weeks (with 3 hours of teaching a chapter per week). The minimal prerequisites for this course are a mastering of basic Probability theory for discrete and continuous variables with the elementary knowledge of the R programming language.
Places where the book has been used
Besides AgroParisTech and Agrocampus-Ouest applied Bayesian classes, this course has been taught by the authors for the Department of Fisheries and Ocean, Canada, at Moncton in 2005, as a two week Bayesian school for researchers. The doctoral sessions for ecological sciences in Paris (2007) and for fisheries sciences in Rennes (2006) also relied on the book's material to promote Bayesian thinking. American Statistician, 2014
International Statistical Review, 2013
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Last update: May 2012