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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 61–70 of 273 papers

TitleStatusHype
Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models—0
Tensor Program Optimization with Probabilistic Programs—0
Designing Perceptual Puzzles by Differentiating Probabilistic Programs—0
Program Analysis of Probabilistic Programs—0
Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming—0
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives—0
A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems—0
Compartmental Models for COVID-19 and Control via Policy Interventions—0
A Probabilistic Programming Idiom for Active Knowledge Search—0
Weighted Programming—0
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