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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
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
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators—0
Incorporating Expert Opinion on Observable Quantities into Statistical Models -- A General Framework—0
Inference Over Programs That Make Predictions—0
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