Wellcome

Introduction to Bayesian Methods in Ecology and Natural Resources [electronic resource] / by Edwin J. Green, Andrew O. Finley, William E. Strawderman.

By: Green, Edwin J [author.]Contributor(s): Finley, Andrew O [author.] | Strawderman, William E [author.] | SpringerLink (Online service)Material type: TextTextPublisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020Description: XII, 183 p. 60 illus., 13 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783030607500Subject(s): Applied ecology | Statistics  | Animal ecology | Forestry management | Applied Ecology | Statistics for Life Sciences, Medicine, Health Sciences | Animal Ecology | Forestry ManagementAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 577 LOC classification: QH541.29Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBookSummary: 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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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.

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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