Stat 375 – Inference in Graphical Models

Andrea Montanari, Stanford University, Spring 2012
Graphical model of a gene network 

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.

Applications/examples will include: Gaussian models with sparse inverse covariance; hidden Markov models (Viterbi and BCJR algorithms, Kalman filter); computer vision (segmentation, tracking, etc); constraint satisfaction problems; machine learning (clustering, classification); communications.

STAT375 is a cognate course in EE.

Class Times and Locations

  • Mon-Wed 9:30AM-10:45AM

  • Green Earth Sciences Room 131


Homework 4 due date was postponed to May 7!!