12 citations · 12 across the 2 of their papers we have counts for
2 papers
cs.LG2019
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs
Robert Walecki, Kostis Gourgoulias, Adam Baker +7
Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty. Inference…
cs.AI2019★ 12 cited
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
Yura Perov, Logan Graham, Kostis Gourgoulias +4
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programmin…