Bayesian statistical modeling and data analysis

Spring 2026

Schedule

Week	Date	Topic	Reading	Notes	Assignment
1	Wed Jan 07	lab: welcome		💻	hello R
	Thu Jan 08	intro, history, notation	Ch. 2		hw 0
2	Mon Jan 12	lab: MLE review		💻
	Tue Jan 13	probability, exchangeability	Ch. 2	🗒 📝	hw 1
	Thu Jan 15	beta-binomial model	Ch. 3	🗒 📝
3	Mon Jan 19	NO CLASS
	Tue Jan 20	Poisson-gamma model, exp families	Ch. 3	🗒 📝	hw 2
	Thu Jan 22	reliability (conf. intervals, hpd, Laplace approx.)	Ch. 3	🗒 📝
4	Mon Jan 26	lab: exp families and transformations		💻
	Tue Jan 27	intro to Monte Carlo	📖	🗒	hw 3
	Thu Jan 29	predictive checks and MC error	Ch. 4	🗒 📝
5	Mon Feb 02	lab: mixture densities		💻
	Tue Feb 03	the normal model	Ch. 5	🗒 📝	hw 4
	Thu Feb 05	normal part II, risk and loss	Ch. 5	📝
6	Mon Feb 09	lab: normal data and estimators		💻
	Tue Feb 10	estimators	Ch. 5	🗒📝
	Thu Feb 12	priors	📖	🗒📝
7	Mon Feb 16	lab: Jeffreys prior; exam review		💻
	Tue Feb 17	review
	Thu Feb 19	exam I
8	Mon Feb 23	lab: recap		💻
	Tue Feb 24	Metropolis algorithm	📖	🗒	hw 5
	Thu Feb 26	MCMC diagnostics	Ch. 6, 10	🗒 📝
9	Mon Mar 02	lab: Metropolis-Hastings		💻💻
	Tue Mar 03	Gibbs sampling	Ch. 6	🗒 📝
	Thu Mar 05	multivariate normal	Ch. 7	🗒	hw 6
10	Mon Mar 09	NO CLASS
	Tue Mar 10	NO CLASS
	Thu Mar 12	NO CLASS
11	Mon Mar 16	lab: office hours & regression review
	Tue Mar 17	Bayesian regression	Ch. 9, background	🗒 📝	hw 7
	Thu Mar 19	hierarchical modeling	Ch. 8	🗒📝
12	Mon Mar 23	lab: rstanarm		💻
	Tue Mar 24	model averaging	Ch. 9 sec. 3	🗒
	Thu Mar 26	mixed effects models	Ch. 11	📝.R
13	Mon Mar 30	lab: probit regression		💻
	Tue Mar 31	Hamiltonian Monte Carlo	📖	🗒
	Thu Apr 02	exam II
14	Mon Apr 06	lab: exam review		💻
	Tue Apr 07	Bayesian inverse problems	📖	🗒	hw 8
	Thu Apr 09	missing data	Ch. 7	📝.R
15	Mon Apr 13	lab: inverse problem		💻
	Tue Apr 14	practice for final