San Francisco Chapter of the American Statistical Association September Seminar



Jie Peng, Department of Statistics, University of California, Davis


Monday September 29, 2014, 4:30-5:00 pm refreshments and networking, 5:00-6:00pm  talk


Stanford HRP (Health Research & Policy) Conference room T138B  map


Learning graphical models




A fundamental question in Statistics is to understand how a set of random variables are interacting with each other. Interactions (in terms of conditional dependencies) can be represented by graphs which provide compact  representations of a probability distribution.  Graphical models have wide applications in many fields including genomics, neuroscience, artificial intelligence, etc.  


In this talk, we will start with an introduction of graphical models, focusing on Markov random fields and Bayesian networks. We will discuss the connection between these two types of models and various algorithms for fitting them. Particularly, we will emphasize structure learning algorithms and the high-dimensional regime. Numerical examples and applications to genomics data   will be mentioned throughout the talk.