Graphical Model (Theory Latent Models)

Thursday, June 20, 2013 - 11:00am - 12:30pm
Lind 305
David Madigan (Columbia University)
Graphical Markov models use graphs with nodes
corresponding to random variables, and edges that
encode conditional independence relationships between
those variables. Directed graphical models (aka Bayesian
networks) in particular have received considerable attention.
This lecture will review basic concepts in graphical
model theory such as Markov properties, equivalence,
and connections with causal inference.
MSC Code: