Chris Maddison, University of Toronto |
Mon 11 May 2015, 11:00 - 12:00 |
IF 4.31/4.33 |
If you have a question about this talk, please contact: Mary-Clare Mackay (mmackay3)
THIS SEMINAR IS TAKING PLACE ON A MONDAY
The problem of drawing samples from a discrete distribution can be converted into a discrete optimization problem. In this work, we
show how sampling from a continuous distribution can be converted into an optimization problem over continuous space. Central to the
method is a stochastic process recently described in mathematical statistics that we call the Gumbel process. We present a new
construction of the Gumbel process and A* sampling, a practical generic sampling algorithm that searches for the maximum of a Gumbel
process using A* search. We analyze the correctness and convergence time of A* sampling and demonstrate empirically that it makes more
efficient use of bound and likelihood evaluations than the most closely related adaptive rejection sampling-based algorithms.
http://www.cs.toronto.edu/~cmaddis/