Choose who walks with you

Statistics & Biostatistics (PhD): 5 courses in the order to take them, starting with Doctoral Mathematical Statistics. Every course has two AI teachers, a woman and a man, who work with you one-on-one by email.

Doctoral Degrees

Statistics & Biostatistics (PhD)

5 courses in order · 10 teachers

Agents in Training

Still learning · not yet available to request

Statistics & Biostatistics (PhD) · courses in order

Doctoral Mathematical Statistics

A Doctoral Mathematical Statistics Teacher takes you through first-year PhD statistical theory — exponential families, sufficiency and completeness, unbiased estimation and the Cramér-Rao bound, decision theory, asymptotics of the MLE, hypothesis testing and confidence sets — by keeping the model, the loss and the conditions in view. They check your derivations and proofs and make practice and qualifying exam plans. AI teaching companions, not your instructor or school.

Measure-Theoretic Probability

A Measure-Theoretic Probability Teacher takes you through graduate probability — probability spaces and random variables, independence and Borel-Cantelli, the laws of large numbers, weak convergence and central limit theorems, conditional expectation and martingales — by naming the mode of convergence and the sigma-algebra in every statement. They check your proofs, build counterexamples with you and make practice and qualifying exam plans. AI teaching companions, not your instructor or school.

Bayesian Statistics

A Bayesian Statistics Teacher takes you through doctoral Bayesian statistics — exchangeability and priors, posterior derivations, decision theory, Bernstein-von Mises, hierarchical models, MCMC and variational inference, and model checking and comparison — by treating the model as something you state, fit, check and defend. They check your derivations, help debug your models and make practice and exam plans. AI teaching companions, not your instructor or school.

Statistical Learning Theory

A Statistical Learning Theory Teacher takes you through doctoral learning theory — the PAC framework, concentration inequalities, VC dimension, Rademacher complexity, margins and kernels, stability, surrogate losses, online learning and lower bounds — by asking what is random, what is fixed and what each bound says in words. They check your proofs, help you read papers and make practice and exam plans. AI teaching companions, not your instructor or school.

Advanced Statistical Computing

An Advanced Statistical Computing Teacher takes you through doctoral statistical computing — floating-point arithmetic, numerical linear algebra, optimization and EM, numerical integration, simulation and Monte Carlo, MCMC, the bootstrap and smoothing — by testing every method on a case with a known answer. They check your derivations, help you debug and speed up your code, read your simulation results and make practice and exam plans. AI teaching companions, not your instructor or school.