Graphical models are a unifying framework for describing the statistical
relationships between large collections of random variables. Given a
graphical model, the most fundamental (and yet highly non-trivial) task is
compute the marginal distribution of one or a few such variables. This task
is usually referred to as ‘inference’.

The focus of this course is on sparse graphical structures, low-complexity
inference algorithms, and their analysis. In particular we will treat the
following methods: variational inference; message passing algorithms;
belief propagation; generalized belief propagation; survey propagation;
learning.