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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 261–270 of 273 papers

TitleStatusHype
ScenicNL: Generating Probabilistic Scenario Programs from Natural Language—0
Graph Tracking in Dynamic Probabilistic Programs via Source Transformations—0
Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming—0
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives—0
High Five: Improving Gesture Recognition by Embracing Uncertainty—0
Hijacking Malaria Simulators with Probabilistic Programming—0
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic—0
How To Train Your Program: a Probabilistic Programming Pattern for Bayesian Learning From Data—0
Identifying latent disease factors differently expressed in patient subgroups using group factor analysis—0
Importance Sampled Stochastic Optimization for Variational Inference—0
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