Class Schedule

This page contains a schedule of the topics, content, and assignments for the semester. Note that this schedule will be updated as necessary the semester progresses, with all changes documented here.

Instructions to save the slides as PDFs can be found here. The key is to hit E when the slides are open in your browser (ideally Chrome, but it may work in others) to toggle into PDF print mode.

Readings can be accessed using the link (when needed, through the Cornell library with a Cornell login) or on Canvas.

Week	Date	Topic	Slides	Reading	Homework
1	1/22	Class Introduction
2	1/27	Null Hypothesis Testing and Statistical Significance		Lloyd & Oreskes (2018)
	1/29	Probability Fundamentals
3	2/3	Probability Models for Data		Shmueli (2010)
	2/5	Time Series
4	2/10	Calibrating Simulation Models
	2/12	Calibrated Residuals
5	2/17	February Break
	2/19	Bayesian Statistics			Gelman & Shalizi (2013)
6	2/24	Bayesian Workflow
	2/26	Random Variate Simulation
7	3/3	Monte Carlo Simulation		Bankes (1993)
	3/5	The Bootstrap
8	3/10	The Parametric Bootstrap		Ruckert et al (2017)
	3/12	Bayesian Computing and Markov Chains
9	3/17	Markov Chain Monte Carlo		Clark et al (2021)
	3/19	Predictive Model Assessments
10	3/24	Scoring and Cross-Validation
	3/26	Information and Entropy
	3/31	Spring Break
	4/2	Spring Break
11	4/7	Information Criteria
	4/9	Modeling Extreme Values
12	4/14	Peaks Over Thresholds
	4/16	Missing Data
13	4/21	Multiple Imputation and Class Review
	4/23	No Class
14	4/28	Project Presentations
	4/30	Project Presentations
15	5/5	Project Presentations