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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 181–190 of 273 papers

TitleStatusHype
Automated learning with a probabilistic programming language: Birch—0
Sinkhorn AutoEncodersCode0
Inference Over Programs That Make Predictions—0
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic ProgrammingCode0
Neural Distribution Learning for generalized time-to-event prediction—0
An Introduction to Probabilistic ProgrammingCode0
Scenic: A Language for Scenario Specification and Scene GenerationCode1
A Fairness-aware Hybrid Recommender System—0
Machine Teaching of Active Sequential LearnersCode0
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard ModelCode0
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