Graduate Course Sites

Part of the teaching portfolio. This page collects the graduate mathematics and statistics course materials I am assembling, in the same style as the undergraduate collection.

The graduate collection is early. The undergraduate course sites came first, and the graduate materials are being built one course at a time rather than announced as a set. Each entry below is labeled with the state it is actually in, so a course whose materials are still being assembled is never presented as a finished public site.

Anything operational for a live section — meetings, dates, assessments, grades, and announcements — belongs in the LMS, not here.

Graduate courses

In development

Mathematical Statistics I

MATH 75063 — Probability, Likelihood, and Estimation

The first course in the graduate mathematical-statistics sequence, and the common theoretical foundation for the graduate curriculum around it: statistical experiments and what a parameter is meant to stand for, a focused probability bridge, sampling distributions and convergence, likelihood, estimation, sufficiency and information, and an introduction to Bayesian decision ideas. Simulation in R is used throughout as a way to reason about the theory rather than as a substitute for it.

It is a theory course by design, not a survey of applied methods. Regression, causal estimation, and computational methods appear only far enough to mark the bridges to the courses that take them up properly.

Materials are still being assembled, so dates, policies, assessments, and readings all remain provisional. There is no public course site for this course yet, and nothing here links to one until it exists.

Where this course leads

Mathematical Statistics I is the course the rest of the graduate statistics curriculum leans on, so it is worth saying where that leads. The map below describes direction, not a schedule.

  • Mathematical Statistics I — in development, described above.
  • Mathematical Statistics II — the second course in the sequence, taking up hypothesis testing, interval procedures, decision theory, asymptotics, and resampling.
  • Later graduate work — regression and generalized linear models, computational statistics, Bayesian modeling, and causal inference, each resting on the estimation and likelihood theory built here.

Only the first entry has materials underway. The rest describe how the curriculum fits together; they are not announced offerings, and none of them has a public course site.

The rest of the portfolio

The Teaching overview has the current courses and the undergraduate course sites — assembled, public course-material sites in probability, inference, modeling, design, and computing. The graduate collection is built in the same style, and will link out to its own course sites the same way once they exist.