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Bayesian Methods for Ecology

ISBN-10: 0521615593
ISBN-13: 9780521615594
Edition: 2007
List price: $57.00 Buy it from $56.95
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Description: The interest in using Bayesian methods in ecology is increasing, however many ecologists have difficulty with conducting the required analyses. McCarthy bridges that gap, using a clear and accessible style. The text also incorporates case studies to  More...

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Book details

List price: $57.00
Copyright year: 2007
Publisher: Cambridge University Press
Publication date: 5/10/2007
Binding: Paperback
Pages: 312
Size: 5.75" wide x 8.75" long x 0.75" tall
Weight: 1.364
Language: English

The interest in using Bayesian methods in ecology is increasing, however many ecologists have difficulty with conducting the required analyses. McCarthy bridges that gap, using a clear and accessible style. The text also incorporates case studies to demonstrate mark-recapture analysis, development of population models and the use of subjective judgement. The advantages of Bayesian methods, are also described here, for example, the incorporation of any relevant prior information and the ability to assess the evidence in favour of competing hypotheses. Free software is available as well as an accompanying web-site containing the data files and WinBUGS codes. Bayesian Methods for Ecology will appeal to academic researchers, upper undergraduate and graduate students of Ecology.

Michael A. McCarthy is Senior Ecologist at the Royal Botanical Gardens, Melbourne and Senior Fellow in the School of Botany at the University of Melbourne.

Preface
Introduction
Logic in determining the presence or absence of a species
Estimation of a mean
Concluding remarks
Critiques of statistical methods
Introduction
Sex ratio of koalas
Null hypothesis significance testing
Information-theoretic methods
Bayesian methods
Estimating effect sizes
Concluding remarks
Analysing averages and frequencies
The average
The Poisson distribution with extra variation
Estimating differences
Required sample sizes when estimating means
Estimating proportions
Multinomial models
Concluding remarks
How good are the models?
How good is the fit?
How complex is the model?
Combining measures of fit and simplicity
The Bayes factor and model probabilities
Evaluating the shape of distributions
Concluding remarks
Regression and correlation
Regression
Correlation
Concluding remarks
Analysis of variance
One-way ANOVA
Coding of variables
Fixed and random factors
Two-way ANOVA
Interaction terms in ANOVA
Variance partitioning
An example of ANOVA: effects of vegetation removal on a marsupial
Analysis of covariance
ANCOVA: a case study
Log-linear models for contingency tables
Concluding remarks
Case Studies
Mark-recapture analysis
Methods
Effects of marking frogs
Logistic regression
Model A
Models B and C
Population dynamics
Mountain pygmy possums
Subjective priors
Eliciting probabilities
Handling differences of opinion
Using subjective judgements
Using the consensus of experts
Representing differences of opinion with subjective priors
Using Bayesian networks to represent expert opinion
Concluding remarks
Conclusion
Prior information
Flexible statistical models
Intuitive results
Bayesian methods make us think
A Bayesian future for ecology
Appendices
A tutorial for running WinBUGS
A summary of steps for running WinBUGS
The steps in more detail
How to write WinBUGS code
Probability distributions
Discrete random variables
Continuous random variables
Univariate discrete distributions
Univariate continuous distributions
Multivariate discrete distributions
Multivariate continuous distributions
Conjugacy
MCMC algorithms
Why does it work?
References
Index

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