ABC World Seminar: Dennis Prangle

on the April 9, 2020

at 11:30 am [UK time]
For this first session of the ABC World Seminar, Dennis Prangle the University of Newcastle will talk about "Distilling importance sampling"

Inspired by the "One World Probability Seminar", we decided to run The ABC World Seminar, a weekly/fortnightly series of seminars that will take place on Blackboard Collaborate on Thursdays at 11.30am [UK time]. The idea is to gather members and disseminate results and innovation during these weeks and months under lockdown.


Dennis Prangle

Abstract
To be efficient, importance sampling requires the selection of an accurate proposal distribution. This talk describes learning proposal distributions using optimisation over a flexible family of densities developed in machine learning: normalising flows. In a likelihood-free context, training data is generated by running ABC importance sampling with a large bandwidth parameter, and this is “distilled”by using it to train the normalising flow. Over many iterations of importance sampling and optimisation, the bandwidth is slowly reduced until an importance sampling proposal for a good ABC approximation to the posterior is generated. The method will be demonstrated for likelihood-free inference on a queueing model. In this
example we infer the parameters, and also the random variables used to simulate data. Thus we effectively learning to control the simulator to produce simulations closely matching the observations.
References

[1] D. Prangle (2019). Distilling important sampling, arXiv:1910.03632.

 

 

Published on May 6, 2020