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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 31–40 of 273 papers

TitleStatusHype
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of ThoughtCode2
A Heavy-Tailed Algebra for Probabilistic Programming—0
Scalable Neural-Probabilistic Answer Set ProgrammingCode1
Push: Concurrent Probabilistic Programming for Bayesian Deep LearningCode0
Bayesian Calibration of MEMS AccelerometersCode0
Automating Model Comparison in Factor GraphsCode0
Sequential Monte Carlo Steering of Large Language Models using Probabilistic ProgramsCode1
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming ApproachCode1
String Diagrams with Factorized Densities—0
Dimensionality Reduction as Probabilistic Inference—0
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