Schedule (subject to change!)

Meeting Notes Topics Homework (out) Homework (due) Readings**
Feb 23 Decision-theoretic notation. Bayes estimator. Parametric families. Example: A/B testing. 1.1, 1.2
Feb 25 Intrinsic loss functions. Examples of derivation of Bayes estimators. Doob's consistency theorem. 2.1, 2.3, 2.5, 3.3
Mar 2 Bring laptop in class* Rapid model development with probabilistic programming (i.e. languages such as BUGS, JAGS, Stan, etc). Example: challenger data. Assignment 1
Mar 4 Bring laptop in class* Hierarchies and mixtures. Examples: launchers data, geyser data. 10.1, 10.2
Mar 9 Model selection and averaging. Example: rabbit holes. 7.1
Mar 11 Bring laptop in class* Model selection and averaging, continued. 7.3
Mar 13 Assignment 1
Mar 16 Nested sampling, IS, SIS. Example: HMMs. Project proposal 6.1, 6.2
Mar 18 SMC, SMC samplers. Some theoretical properties. Example:tree inference.
Mar 23 Particle MCMC. Proof of correctness. Particle genealogies. Project proposal
Mar 25 Reversible jump MCMC. Example: multiplicative proposals. Assignment 2 6.3
Mar 30 Reversible jump MCMC vs. Bayesian non-parametric methods.
Apr 1 Bayesian non-parametrics continued.
Apr 6 University closed.
Apr 8 Lab 1 Sampling in non-conjugate BNP models, hierarchical models and the sequence memoizer. Example: language modelling. Assignment 3 (optional)
Apr 13 Lab 2 Second lab: 5pm, computer room on main floor of ESB
Apr 16 Assignment 2
Apr 20 Assignment 3 (optional)
Apr 25 Project deadline




*If you do not have a laptop, you can alternatively form a team with someone with a laptop and work in team. If you have difficulties forming such a team, contact me.
**Unless noted otherwise, readings are in 'The Bayesian Choice', C.P. Robert.