26 January -Bayesian inference
Bayes Intro (pdf notes)
Reading: Kéry and M. Schaub (2011), Chapters 1-2 (thoroughly); review Chapters 3 and 4 especially sections on random effects models, and for tips on simulating data and coding models in R and BUGS.
Bayes basics (pdf notes; from a book on decision analysis, so ignore references to 'previous lab' and 'probabilistic networks')
R script for examples
R program to do glm and Bayesian analysis for fixed time effect
Input file with count data
R program to do glm and Bayesian analysis for random time effect
Input file with count data
Link et al. nest parasitism paper using empirical Bayes
27 January - Sampling and experimental design
Reading: Chapter 5 Conroy and Carroll (2009)
Excel programs for sample size computation and allocation
Note: these spreadsheets don't really do anything novel, they are just to illustrate that you can perform the same calculations either in R or in Excel