Introduction to Hierarchical Bayesian Modeling for Ecological Data

Author:   Eric Parent ,  Etienne Rivot
Publisher:   Taylor & Francis Ltd
ISBN:  

9780367576714


Pages:   428
Publication Date:   30 June 2020
Format:   Paperback
Availability:   In Print   Availability explained
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Introduction to Hierarchical Bayesian Modeling for Ecological Data


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Author:   Eric Parent ,  Etienne Rivot
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Weight:   0.790kg
ISBN:  

9780367576714


ISBN 10:   0367576716
Pages:   428
Publication Date:   30 June 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

I Basic Blocks of Bayesian Modeling: Bayesian Hierarchical Models in Statistical Ecology. The Beta-Binomial Model. The Basic Normal Model. Working with More Than One Beta-Binomial Element. Combining Various Sources of Information. The Normal Linear Model. Nonlinear Models for Stock-Recruitment Analysis. Getting beyond Regression Models. II More Elaborate Hierarchical Structures: HBM I: Borrowing Strength from Similar Units. HBM II: Piling up Simple Layers. HBM III: State-Space Modeling. Decision and Planning. Appendices. Bibliography. Index.

Reviews

This book is a welcome addition to the Bayesian literature. It is well written and amply illustrates Bayesian methods with practical applications in fisheries management. The programs for data analyses are available on the book's website, allowing users to get their 'hands dirty' and in the process really understand the model construction and the software. - Subhash R. Lele, Ecology, 95(1), 2014 The book is well written and easy to read, and the material presented deserves a greater exposure in taught statistics courses. I thoroughly recommend the book and believe that the statistical techniques and their application to quantitative fisheries science could ideally complement a short undergraduate course in applied statistics. -Carl M. O'Brien, International Statistical Review (2013), 81


Author Information

Éric Parent is head of the Research Laboratory for Risk Management in Environmental Sciences (Team MORSE) and a professor in applied statistics and probabilistic modeling for environmental engineering at the National Institute for Rural Engineering, Water and Forest Management (ENGREF/AgroParisTech) in Paris, France. Dr. Parent’s research encompasses Bayesian theory and applications, with special emphasis on environmental systems modeling. Étienne Rivot is a researcher in the Fisheries Ecology Laboratory at Agrocampus Ouest in Rennes, France. Dr. Rivot’s research focuses on the application of Bayesian statistical modeling for the analysis of ecological data, inference, and predictions.

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