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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 131–140 of 273 papers

TitleStatusHype
Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs—0
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support—0
Supervised Bayesian Specification Inference from Demonstrations—0
Surrogate Likelihoods for Variational Annealed Importance Sampling—0
Survival prediction and risk estimation of Glioma patients using mRNA expressions—0
Swift: Compiled Inference for Probabilistic Programming Languages—0
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo—0
Tensor Program Optimization with Probabilistic Programs—0
Tensor Variable Elimination for Plated Factor Graphs—0
TerpreT: A Probabilistic Programming Language for Program Induction—0
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