FIT3229 · Monash University
Bayesian modelling and inference
- Credit points
- 6
- Level
- 3
- Workload
- —
- Faculty
- Information Technology
What it covers
This unit introduces Bayesian approaches to statistical modelling, estimation, and prediction, with an emphasis on practical data analysis and computational methods. It develops the core ideas of prior distributions, posterior inference, Bayesian estimation, predictive distributions, credible intervals, and Bayesian approaches to hypothesis testing. You will apply these ideas in a range of modelling settings, including Bayesian regression models, latent variable models, Gaussian processes, kernel methods, and functional priors. The unit also introduces computational techniques used when exact Bayesian inference is not available, including Markov chain Monte Carlo, variational Bayes, and modern simulation-based approaches such as prior fitted networks. The focus is on developing conceptual understanding and practical modelling skills for students with a computing or data analysis background, rather than on advanced mathematical theory. By the end of the unit, you will be able to specify Bayesian models, interpret posterior and predictive uncertainty, apply approximate inference methods, and communicate Bayesian analyses clearly and responsibly.
Assessment
Assessment not published yet.
When and where
Not offered in 2027, or offerings not published yet.