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Bayesian Population Analysis using WinBUGS: A Hierarchical Perspective
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About Bayesian Population Analysis Using WinBUGS: A
Product Description Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS, and its open-source sister OpenBugs, is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics. Review A comprehensive collection of richly commented Bayesian analyses of ecological models for population analysis using WinBUGS run from within program R, bridging the gap between introductory- and advance-level texts From the Back Cover Bayesian Population Analysis using WinBUGS is an introduction to the analysis of distribution, abundance, and population dynamics of animals and plants using hierarchical models implemented in the leading Bayesian software WinBUGS. It will be of interest to quantitative scientists working in the fields of population ecology, conservation biology, evolutionary biology, population management, disease ecology, fisheries or wildlife biology. The accessible text is ideal for self-study and advanced graduate-level courses. This book: Is a statistical modeling book written by ecologists for ecologists Contains analyses of simulated data, along with fully commented R code for the generation of these data sets, as well as analyses of real data Fully integrates with program Rall analyses are conducted by calling WinBUGS from R Illustrates the tremendous modeling freedom given to ecologists when using the simple and flexible BUGS language Marc Kéry and Michael Schaub are population ecologists with the Swiss Ornithological Institute. Together, they have authored over 120 peer-reviewed journal articles on a wide range of topics, including the analysis of large-scale monitoring programs, demographic population analyses, experimental design for animal and plant surveys, and the population ecology of rare species, as well as Introduction to WinBUGS for Ecologists (Academic Press, 2010).| Bayesian Population Analysis using WinBUGS is an introduction to the analysis of distribution, abundance, and population dynamics of animals and plants using hierarchical models implemented in the leading Bayesian software WinBUGS. It will be of interest to quantitative scientists working in the fields of population ecology, conservation biology, evolutionary biology, population management, disease ecology, fisheries or wildlife biology. The accessible text is ideal for self-study and advanced graduate-level courses. This book: Is a statistical modeling book written by ecologists for ecologists Contains analyses of simulated data, along with fully commented R code for the generation of these data sets, as well as analyses of real data Fully integrates with program Rall analyses are conducted by calling WinBUGS from R Illustrates the tremendous modeling freedom given to ecologists when using the simple and flexible BUGS language Marc Kéry and Michael Schaub are population ecologists with the Swiss Ornithological Institute. Together, they have authored over 120 peer-reviewed journal articles on a wide range of topics, including the analysis of large-scale monitoring programs, demographic population analyses, experimental design for animal and plant surveys, and the population ecology of rare species, as well as Introduction to WinBUGS for Ecologists (Academic Press, 2010). About the Author Marc Kéry is a population ecologist with the Swiss Ornithological Institute and a courtesy professor at the University of Zürich. He is an expert in the estimation and modeling of abundance, distribution and species richness in animal and plant populations and has coauthored approximately 100 peer-reviewed articles and four books. Michael Schaub is the Head of the Ecology Department at the Swiss Ornithological Institute and a courtesy Professor at the University of Bern. His research interests include population dynamics, capture-recapture models, integrated
