Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs

16 Oct 2019Robert WaleckiKostis GourgouliasAdam BakerChris HartChris LucasMax ZwiesseleAlbert BuchardMaria LomeliYura PerovSaurabh Johri

Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty. Inference methods used in PPL can be computationally costly due to significant time burden and/or storage requirements; or they can lack theoretical guarantees of convergence and accuracy when applied to large scale graphical models... (read more)

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