Bayesian methods and modern statistics

Fall 2024

Schedule

Week	Date	Topic	Reading	Notes	Assignment
1	Mon Aug 26	intro, history, notation	Ch. 2		hw 0
	Wed Aug 28	probability, exchangeability	Ch. 2	🗒 📝	hw 1
	Thu Aug 29	lab: welcome		💻	hello R
2	Mon Sep 02	NO CLASS	Ch. 3
	Wed Sep 04	beta-binomial model	Ch. 3	🗒 📝	hw 2
	Thu Sep 05	lab: MLE and MAP estimator		💻
3	Mon Sep 09	Poisson-gamma model, exp families	Ch. 3	🗒 📝
	Wed Sep 11	reliability (conf. intervals, hpd, Laplace approx.)	Ch. 3	🗒 📝	hw 3
	Thu Sep 12	lab: exp. families and transformations		💻
4	Mon Sep 16	intro to Monte Carlo	Ch. 4	🗒 📝
	Wed Sep 18	predictive checks and MC error	Ch. 4	🗒	hw 4
	Thu Sep 19	lab: mixture densities		💻
5	Mon Sep 23	the normal model	Ch. 5	🗒📝
	Wed Sep 25	the normal model II	Ch. 5	📝
	Thu Sep 26	lab: normal data		💻
6	Mon Sep 30	review
	Wed Oct 02	Exam I
	Thu Oct 03	NO LAB
7	Mon Oct 07	Metropolis algorithm	Ch. 10	🗒
	Wed Oct 09	MCMC diagnostics	Ch. 6	🗒	hw 5
	Thu Oct 10	lab: Metropolis algo.		💻
8	Mon Oct 14	NO CLASS
	Wed Oct 16	wrap-up diagnostics and MH	Ch. 6, 10	📝
	Thu Oct 17	lab: MCMC and conf. bands		💻
9	Mon Oct 21	Gibbs sampling	Ch. 6	🗒
	Wed Oct 23	multivariate normal	Ch. 7	🗒	hw 6
	Thu Oct 24	lab: MCMC diagnostics		💻
10	Mon Oct 28	intro to Bayesian regression	Ch. 9	🗒📝
	Wed Oct 30	Bayesian regression II	Ch. 9	🗒	hw 7
	Thu Oct 31	lab: rstanarm		💻
11	Mon Nov 04	estimators	Ch. 5 sec. 4	🗒📝
	Wed Nov 06	hierarchical modeling	Ch. 8	🗒📝	hw 8
	Thu Nov 07	lab: estimators		💻
12	Mon Nov 11	review
	Wed Nov 13	Exam II
	Thu Nov 14	NO LAB
13	Mon Nov 18	priors		🗒
	Wed Nov 20	Bayesian inverse problems	📖	🗒	hw 9
	Thu Nov 21	lab: Bayesian inverse practice		💻
14	Mon Nov 25	Hamiltonian Monte Carlo	📖	🗒
	Wed Nov 27	NO CLASS
	Thu Nov 28	NO CLASS
15	Mon Dec 02	model averaging	Ch. 9 sec. 3	🗒
	Wed Dec 04	practice for final		🗒
	Thu Dec 05	lab: review