Dr Drovandi's teaching philosophy is centred on learning through engagement. He is an advocate of blended learning, combining traditional teaching methods with group activities designed to maximise student participation, as well as the creation of online resources such as teaching videos and facebook groups. 

Computational Bayesian Statistics (Honours/Masters level)

  • Monte Carlo Integration
  • Importance Sampling and Adaptive Importance Sampling
  • Markov chain Monte Carlo
  • Sequential Monte Carlo
  • Approximate Bayesian Computation

Probability & Stochastic Modelling 1 (Undergraduate level)

  • Probability
  • Univariate Discrete and Continuous Random Variables
  • Bivariate Discrete Distributions
  • Chi-square Goodness-of-fit
  • Markov Chains
  • Poisson Processes

Probability & Stochastic Modelling 2 (Undergraduate level)

  • Generating Functions
  • Transformation of Random Variables
  • Order Statistics
  • Markov Chains including Simple Random Walks and Branching Processes
  • Continuous Time Markov Chains
  • Brownian Motion

Generalised Linear Models (Undergraduate level)

  • Normal regression
  • Poisson and binomial regression
  • Multinomial regression
  • log-linear models
  • R Programming