Introduction to Bayesian Methods in Ecology and Natural Resources
Description
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.
This monograph 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.
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Persons
Andrew O. Finley is a Professor at Michigan State University with appointments in the Department of Forestry and Department of Geography, Environment, and Spatial Sci-ences. 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 modeling environmental processes, Bayesian statistics, spatial sta-tistics, and statistical computing.
William E. Strawderman is a Distinguished Professor in and former chair of the De-partment of Statistics at Rutgers University. His theoretical research focuses on Bayes-ian methods, Statistical Decision Theory and Multivariate Analysis, particularly related to Simultaneous estimation. Much of his applied research has been on Bayes and Em-pirical Bayes methods in Forestry. He is a Fellow of the American Statistical Associa-tion and the Institute of Mathematical Statistics.
Content
1. Introduction.- 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.