Speaker |
Ted Meeds |
|---|---|
Affiliation |
University of Amsterdam |
Date |
Friday, 06 May 2016 |
Time |
13:00-14:00 |
Location |
Zoom |
Link |
Roberts G08 Sir David Davies LT |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
Likelihood-free inference, or approximate Bayesian computation (ABC), is a general framework for performing Bayesian inference in simulation-based science. In this talk I will discuss two new approaches to likelihood-free inference that involve explicit control over a simulation’s randomness. By re-writing simulation code with two sets of arguments, the simulation parameters and its random numbers, many algorithmic options open up. The first approach, called Optimisation Monte Carlo, in an algorithm that efficiently and independently samples parameters from the posterior by first sampling a set of random numbers from a prior distribution, then running an optimisation algorithm---with fixed random numbers---to match simulation statistics with observed statistics. The second approach is recent and ongoing research on a variational ABC algorithm that has been written in an auto-differentiation language allowing for the gradients of the variational parameters to be computed through the simulation code itself. |
Biography |