Lecture 13

statistical decision theory: basics

Lecture 14

risk functions

Lecture 15

tensor product of experiments, sample complexity

Lecture 16

sample complexity, f-divergence, hypothesis testing, connection between f-divergences

Lecture 17

connection between f-divergences, variational form of f-divergence

Lecture 18

f-divergence, parameter estimation, HCR bound, CR lower bound, fisher information

Lecture 19

Fisher information, multivariate HCR bound

Lecture 20

Bayesian CR lower bound, information bound, local estimators, biased estimators

Lecture 21

maximum likelihood estimator, high-dimensional unstructured estimation, bowl-shaped loss

Lecture 22

two-point quantization of the estimation problem (LeCam’s method)

Lecture 23

mutual information method, Fano's method, density estimation