Probability Seminar

From UW-Math Wiki
Revision as of 13:05, 15 December 2014 by Pmwood (talk | contribs)
Jump to: navigation, search

Spring 2015

Thursdays in 901 Van Vleck Hall at 2:25 PM, unless otherwise noted.

If you would like to sign up for the email list to receive seminar announcements then please send an email to

Thursday, January 15, Miklos Racz, UC-Berkeley Stats

Title: TBA

Monday, December 1, Joe Neeman, UT-Austin, 4pm, Room B239 Van Vleck Hall

Please note the unusual time and room.

Title: Some phase transitions in the stochastic block model

Abstract: The stochastic block model is a random graph model that was originally 30 years ago to study community detection in networks. To generate a random graph from this model, begin with two classes of vertices and then connect each pair of vertices independently at random, with probability p if they are in the same class and probability q otherwise. Some questions come to mind: can we reconstruct the classes if we only observe the graph? What if we only want to partially reconstruct the classes? How different is this model from an Erdos-Renyi graph anyway? The answers to these questions depend on p and q, and we will say exactly how.

Thursday, December 4, Arjun Krishnan, Fields Institute

Title: Variational formula for the time-constant of first-passage percolation

Abstract: Consider first-passage percolation with positive, stationary-ergodic weights on the square lattice in d-dimensions. Let [math]T(x)[/math] be the first-passage time from the origin to [math]x[/math] in [math]Z^d[/math]. The convergence of [math]T([nx])/n[/math] to the time constant as [math]n[/math] tends to infinity is a consequence of the subadditive ergodic theorem. This convergence can be viewed as a problem of homogenization for a discrete Hamilton-Jacobi-Bellman (HJB) equation. By borrowing several tools from the continuum theory of stochastic homogenization for HJB equations, we derive an exact variational formula (duality principle) for the time-constant. Under a symmetry assumption, we will use the variational formula to construct an explicit iteration that produces the limit shape.


Past Seminars