Bayesian statistics

Learn probabilistic programming, how to build and apply Bayesian statistical models, and provide statistical support to researchers and professionals.

  • The process and limitations of statistical inference (experiment design, data gathering, model selection, computation, interpretation, what can and can’t we claim …).
  • Probabilistic thinking.
  • Probabilistic programming and Stan basics.
  • General linear models (GLM), regularization.
  • Survey sampling.
  • Hierarchical (multilevel) models.
  • Questionnaire design.
  • Choosing priors and eliciting probabilities from experts.
  • Objective/subjective Bayesian statistics.
  • Time series models (seasonality, trends, AR, MA, ARMA, ARIMA).
  • Model evaluation and selection.
  • Modelling censored data (survivability, reliability).
  • Hamiltonian Monte Carlo (HMC), No U-Turn Sampler (NUTS), advanced Markov Chain Monte Carlo (MCMC) diagnostics.
  • Combining models.
  • Hands on work and projects.