Bayesian Methods for 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.
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ANOVA approximately assumed Bayes Bayes factor Bayesian analysis Bayesian methods Bernoulli beta distribution body mass calculate capture rate coefficient confidence interval considered correlation credible interval detection deviance diameter of trees DIC values dnorm(0 ecologists ecology equal example explanatory variables fish kill frequentist frequentist methods given helmeted honeyeater hypothesis significance testing individuals initial values interaction terms koalas kurtosis likelihood linear locations Markov chain McCarthy mean diameter mulgara normal distribution null hypothesis significance null hypothesis testing occur p-value parameter estimates Pfiesteria PLOs plot Poisson distribution population possible posterior distribution posterior probability powerful owls prec precision predicted present prior distribution prior information prior probability probability distribution probability of obtaining proportion provides quadrats Quinn and Keough random variables reference class relationship relative resighting return rate sample sex ratio species specified standard deviation subjective judgement toe clipping toe removed uncertainty uninformative prior variance variation WinBUGS code zero
Page x - The probability distribution function, /'< 4. r). is defined as the probability that the random variable x is less than some value...
Page 6 - The constant pdf (the flat line) shows that the standard uniform distribution is a special case of the beta distribution.
Page 5 - AZ) sin a] (53) where A is the lower limit and B is the upper limit of the particular integral in question, and the a range of integration is 0 to Tt/2.