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E-grāmata: Bayesian Methods for Ecology

3.62/5 (16 ratings by Goodreads)
(University of Melbourne)
  • Formāts: PDF+DRM
  • Izdošanas datums: 10-May-2007
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9780511282546
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Bayesian Methods for EcologyPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 10-May-2007
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9780511282546
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An accessible text describing how to use Bayesian methods of statistical analysis in ecology.

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.

Recenzijas

"[ This book] will advance any ecologists' understanding of Bayesian statistics. ... the many diverse examples, which are the book's greatest strength, make the topic very approachable, even for people with moderate understanding of statistical theory. ... I therefore would highly recommend it to any ecologist interested in learning more about Bayesian statistics, and especially to those who want to learn to run Bayesian analyses in Win BUGS." - Tabitha Graves, Ecology

Papildus informācija

An accessible text describing how to use Bayesian methods of statistical analysis in ecology.
Preface xi
1 Introduction
1
Example 1: Logic in determining the presence or absence of a species
4
Example 2: Estimation of a mean
20
Concluding remarks
29
2 Critiques of statistical methods
30
Introduction
30
Sex ratio of koalas
31
Null hypothesis significance testing
35
Information-theoretic methods
45
Bayesian methods
52
Estimating effect sizes
58
Concluding remarks
61
3 Analysing averages and frequencies
63
The average
63
The Poisson distribution with extra variation
71
Estimating differences
71
Required sample sizes when estimating means
73
Estimating proportions
81
Multinomial models
88
Concluding remarks
92
4 How good are the models?
94
How good is the fit?
95
How complex is the model?
101
Combining measures of fit and simplicity
105
The Bayes factor and model probabilities
108
Evaluating the shape of distributions
116
Concluding remarks
118
5 Regression and correlation
119
Regression
119
Correlation
148
Concluding remarks
156
6 Analysis of variance
158
One-way ANOVA
158
Coding of variables
159
Fixed and random factors
162
Two-way ANOVA
165
Interaction terms in ANOVA
167
Variance partitioning
167
An example of ANOVA: effects of vegetation removal on a marsupial
170
Analysis of covariance
180
ANCOVA: a case study
182
Log-linear models for contingency tables
190
Concluding remarks
193
CASE STUDIES
7 Mark-recapture analysis
197
Methods
197
8 Effects of marking frogs
207
Logistic regression
209
Model A
210
Models B and C
211
9 Population dynamics
217
Mountain pygmy possums
217
10 Subjective priors 225
Eliciting probabilities
225
Handling differences of opinion
226
Using subjective judgements
227
Using the consensus of experts
227
Representing differences of opinion with subjective priors
230
Using Bayesian networks to represent expert opinion
236
Concluding remarks
243
11 Conclusion 244
Prior information
244
Flexible statistical models
245
Intuitive results
245
Bayesian methods make us think
245
A Bayesian future for ecology
246
APPENDICES
A A tutorial for running WinBUGS
249
A summary of steps for running WinBUGS
249
The steps in more detail
249
How to write WinBUGS code
253
B Probability distributions
255
Discrete random variables
255
Continuous random variables
257
Univariate discrete distributions
261
Univariate continuous distributions
266
Multivariate discrete distributions
272
Multivariate continuous distributions
273
Conjugacy
275
C MCMC algorithms
277
Why does it work?
280
References 282
Index 293
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.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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