Bayesian Biostatistics 2014

By | February 2, 2014

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):


  1. Introduction: Course contents, pros and cons of Bayes, necessary skills.
  2. Normal distribution: Introducing likelihood and deviance on the Normal example.
  3. Poisson distribution: The didactic simplicity of Poisson and its likelihood. Likelihood maximization.
  4. Doing it the Bayesian way: Elements of conditional probability, Bayes theorem, and MCMC sampling.
  5. Bayesian resources: Overview of software, books and on-line resources.


  1. t-test: First model with 'effects', hypotheses testing, derived variables.
  2. Linear regression: Extracting credible and prediction intervals.
  3. ANOVA: Fixed effects, random effects, the effect of small sample, and shrinkage.
  4. Time series analysis: Fitting more complex function. Auto-regression.


  1. Site occupancy model: accounting for imperfect detection.
  2. The rest of probability distributions (binomial, beta, gamma, multivariate normal, negative binomial).
  3. Convergence diagnostics, publishing Bayesian papers.
  4. Concluding remarks.
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