Introduction to Bayesian Methods in Ecology and Natural Resources - Hardcover

Green, Edwin J.; Finley, Andrew O.; Strawderman, William E.

Hardcover

ISBN 10: 3030607496 ISBN 13: 9783030607494

Publisher: Springer, 2020

Synopsis

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.

About this title

About the Author

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 ex-tensively on Bayes and Em`pirical Bayes methods in Forestry since the mid-1980s. He is a  Fellow of the American Statistical Association and the Society of American For-esters, and has been Editor and Asssociate Editor of Forest Science and Associate Edi-tor of Environmental and Ecological Statistics. He taught a graduate course on Bayesi-an 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 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.

From the Back Cover

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.

Edition details

• Publisher: Springer
• Publication date: 2020
• ISBN 10: 3030607496
• ISBN 13: 9783030607494
• Binding: Hardcover
• Language: English
• Edition number: 1
• Number of pages: 195