Bayesian methods and modern statistics

Spring 2025

Schedule

Week	Date	Topic	Reading	Notes	Assignment
1	Wed Jan 08	NO CLASS
	Thu Jan 09	lab: welcome		💻	hello R
	Fri Jan 10	intro, history, notation	Ch. 2		hw 0
2	Wed Jan 15	probability, exchangeability	Ch. 2	🗒 📝	hw 1
	Thu Jan 16	lab: MLE		💻
	Fri Jan 17	beta-binomial model	Ch. 3	🗒 📝
3	Wed Jan 22	Poisson-gamma model, exp families	Ch. 3	🗒 📝	hw 2
	Thu Jan 23	lab: exp families and transformations		💻
	Fri Jan 24	reliability (conf. intervals, hpd, Laplace approx.)	Ch. 3	🗒 📝
4	Wed Jan 29	intro to Monte Carlo	📖	🗒 📝	hw 3
	Thu Jan 30	lab: mixture densities		💻
	Fri Jan 31	predictive checks and MC error	Ch. 4	🗒 📝
5	Wed Feb 05	the normal model	Ch. 5	🗒📝	hw 4
	Thu Feb 06	lab: normal data		💻
	Fri Feb 07	the normal model II	Ch. 5	📝
6	Wed Feb 12	estimators	Ch. 5	🗒📝
	Thu Feb 13	lab: estimators		💻
	Fri Feb 14	review
7	Wed Feb 19	Metropolis algorithm	Ch. 10	🗒
	Thu Feb 20	NO LAB
	Fri Feb 21	Exam I
8	Wed Feb 26	finish Metropolis algo.	Ch. 6, 10		hw 5
	Thu Feb 27	lab: Metropolis-Hastings and conf. bands		💻 💻
	Fri Feb 28	MCMC diagnostics	Ch. 6, 10	🗒📝
9	Wed Mar 05	Gibbs sampling	Ch. 6	🗒
	Thu Mar 06	lab: MCMC diagnostics		💻
	Fri Mar 07	multivariate normal	Ch. 7	🗒	hw 6
10	Wed Mar 12	NO CLASS
	Thu Mar 13	NO CLASS
	Fri Mar 14	NO CLASS
11	Wed Mar 19	Bayesian regression I	Ch. 9	🗒	hw 7
	Thu Mar 20	lab: rstanarm		💻
	Fri Mar 21	Bayesian regression II	Ch. 9	🗒📝
12	Wed Mar 26	hierarchical modeling	Ch. 8	🗒📝
	Thu Mar 27	lab: probit regression		💻
	Fri Mar 28	review
13	Wed Apr 02	Exam II
	Thu Apr 03	NO LAB
	Fri Apr 04	model averaging	Ch. 9 sec. 3	🗒
14	Wed Apr 09	Bayesian inverse problems	📖	🗒	hw 8
	Thu Apr 10	lab: inverse problems		💻
	Fri Apr 11	Hamiltonian Monte Carlo	📖	🗒
15	Wed Apr 16	priors		🗒	hw 9
	Thu Apr 17	review / office hours
	Fri Apr 18	missing data		📝.R
16	Wed Apr 23	Practice for final