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Probabilistic Programming

Probabilistic programming languages are designed to describe probabilistic models and then perform inference in those models. PPLs are closely related to graphical models and Bayesian networks, but are more expressive and flexible.

( Image credit: Michael Betancourt )

Papers

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Showing 171–180 of 273 papers

TitleStatusHype
Towards an architectural framework for intelligent virtual agents using probabilistic programming—0
Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models—0
Transforming Probabilistic Programs for Model Checking—0
Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models—0
Uncertainty Analysis in SPECT Reconstruction based on Probabilistic Programming—0
Unifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representations—0
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs—0
Using probabilistic programs as proposals—0
Venture: a higher-order probabilistic programming platform with programmable inference—0
Weighted Programming—0
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