Bayesian Analysis for Population Ecology
Chapman & Hall/CRC, Sep 15, 2009 - Mathematics - 442 pages
Modern Bayesian methods have an important role to play in population ecology. Statistical methods for the analysis of mark-recapture-recovery data on wild animals continue to develop in response to the availability of long-term data sets and advances in animal marking and tracking techniques. Bringing together top experts in the field, this book presents up-to-date Bayesian procedures in an accessible manner, illustrated by a wide range of real examples and complemented by accessible computer programs. The authors include WinBUGS and R code for all of the analyses that are performed in the text.
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Data Models and Likelihoods
Classical Inference Based on Likelihood
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acceptance probability additional alternative animals assume Bayes factor Bayesian p-value birds burn-in calculate capture capture-recapture data conjugate prior consider convergence corresponding posterior covariate values credible interval data set defined demographic parameters denotes described different models dipper data example fdays first-year survival probability function HPDI initial lapwing likelihood likhood log-likelihood logistic regression marginal posterior Markov chain matrix MCMC algorithm MH algorithm model C/C model move model parameters model uncertainty model-averaged Monte Carlo error multinomial Note number of parameters obtain output p-value param parameter values pilot tuning population possible models posterior distribution posterior estimates posterior mean posterior model probabilities posterior probability prior distribution prior probability prior specification proposal distribution proposal parameters random effects recapture probability recovery probabilities regression coefficients reversible jump ring-recovery data RJMCMC sample saturated model Section simply single-update Soay sheep specify standard deviation summary statistics Table trace plot typically updating algorithm variance vector white stork WinBUGS