Module overview
This module develops some mathematical foundations of statistical inference: the theory of learning from data under uncertainty. We begin by studying a selection of useful tools and techniques from probability theory, including moment generating functions and transformations of random variables. Then we proceed to explore fundamental methods for point estimation, interval estimation and hypothesis testing, with particular emphasis on maximum likelihood theory. We also introduce the framework of Bayesian inference and discuss the frequentist and subjective interpretations of probability.