This book presents modern Bayesian analysis in a format that is accessible to researchers in the fields of ecology, wildlife biology, and natural resource management. Bayesian analysis has undergone a remarkable transformation since the early 1990s. Widespread adoption of Markov chain Monte Carlo techniques has made the Bayesian paradigm the viable alternative to classical statistical procedures for scientific inference. The Bayesian approach has a number of desirable qualities, three chief ones being: i) the mathematical procedure is always the same, allowing the analyst to concentrate on the scientific aspects of the problem; ii) historical information is readily used, when appropriate; and iii) hierarchical models are readily accommodated.
Introduction to Bayesian Methods in Ecology and Natural Resources contains numerous worked examples and the requisite computer programs. The latter are easily modified to meet new situations. A primer on probability distributions is also included because these form the basis of Bayesian inference.
Researchers and graduate students in Ecology and Natural Resource Management will find this book a valuable reference.
2. Probability Theory and Some Useful Probability Distribution
3. Choice of Prior Distribution
4. Elementary Bayesian Analyses
5. Hypothesis Testing and Model Choice
6. Linear Models
7. General Linear Models
8. Spatial Models
Edwin J. Green is Professor Emeritus, Rutgers University where he was a member of Graduate Programs in Ecology and Evolution, and in Statistics. He has published extensively on Bayes and empirical Bayes methods in forestry since the mid-1980s. He is a Fellow of the American Statistical Association and the Society of American Foresters, and has been Editor and Associate Editor of Forest Science and Associate Editor of Environmental and Ecological Statistics. He taught a graduate course on Bayesian methods in ecology in the Ecology and Evolution Graduate Program for over two decades.
Andrew O. Finley is a Professor at Michigan State University with appointments in the Department of Forestry and Department of Geography, Environment, and Spatial Sciences. He is also a member of the interdisciplinary Ecology, Evolutionary Biology, and Behavior Graduate Program faculty. His work focuses on developing methodologies for monitoring and modelling environmental processes, Bayesian statistics, spatial statistics, and statistical computing.
William E. Strawderman is a Distinguished Professor in and former chair of the Department of Statistics at Rutgers University. His theoretical research focuses on Bayesian methods, statistical decision theory and multivariate analysis, particularly related to Simultaneous estimation. Much of his applied research has been on Bayes and empirical Bayes methods in forestry. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics.