This post contains materials for Bayesian stats course that I taught between 2-4 Feb 2014 at Faculty of Science, Charles University, Prague, Czech Republic. There were around 40 participants. The complete materials and their source codes (Markdown and R) are on a GitHub repository. The lectures can also be accessed directly as follows (I recommend Chrome):
- Introduction: Course contents, pros and cons of Bayes, necessary skills.
- Normal distribution: Introducing likelihood and deviance on the Normal example.
- Poisson distribution: The didactic simplicity of Poisson and its likelihood. Likelihood maximization.
- Doing it the Bayesian way: Elements of conditional probability, Bayes theorem, and MCMC sampling.
- Bayesian resources: Overview of software, books and on-line resources.
- t-test: First model with 'effects', hypotheses testing, derived variables.
- Linear regression: Extracting credible and prediction intervals.
- ANOVA: Fixed effects, random effects, the effect of small sample, and shrinkage.
- Time series analysis: Fitting more complex function. Auto-regression.
- Site occupancy model: accounting for imperfect detection.
- The rest of probability distributions (binomial, beta, gamma, multivariate normal, negative binomial).
- Convergence diagnostics, publishing Bayesian papers.
- Concluding remarks.