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.