Mathematical Statistics Lecture -

A deep lecture does not end with worship of frequencyist methods. The professor will step back and introduce decision theory : a loss function ( L(\theta, a) ), a risk ( R(\theta, \delta) = \mathbbE_\theta[L(\theta, \delta(X))] ). An estimator is admissible if no other estimator has uniformly lower risk. The Bayes estimator —minimizing posterior expected loss—emerges as a natural solution.