Asymptotically exact inference in differentiable generative models

Understand

Many generative models can be expressed as a differentiable function of random inputs drawn from some simple probability density.

• This framework includes both deep generative architectures such as Variational Autoencoders and a large class of procedurally defined simulator models.
• We present a method for performing efficient MCMC inference in such models when conditioning on observations of the model output.
• For some models this offers an asymptotically exact inference method where Approximate Bayesian Computation might otherwise be employed.