Dr Dennis Prangle from the University of Newcastle will give a talk entitled: Distilling importance sampling. The abstract can be found below.
To be efficient, importance sampling requires the selection of an accurate proposal distribution. This talk describes learning a proposal distribution using optimisation over a flexible family of densities developed in machine learning: normalising flows. Training data is generated by running importance sampling on a tempered version of the target distribution, and this is “distilled” by using it to train the normalising flow. Over many iterations of importance sampling and optimisation, the amount of tempering is slow reduced until an importance sampling proposal for an accurate target distribution is generated. An application to likelihood-free inference is also presented.