Variational Program Inference

Understand

We introduce a framework for representing a variety of interesting problems as inference over the execution of probabilistic model programs.

• We represent a "solution" to such a problem as a guide program which runs alongside the model program and influences the model program's random choices, leading the model program to sample from a different distribution than from its priors.
• Ideally the guide program influences the model program to sample from the posteriors given the evidence.
• We show how the KL- divergence between the true posterior distribution and the distribution induced by the guided model program can be efficiently estimated (up to an additive constant) by sampling multiple executions of the guided model program.