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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 161–170 of 273 papers

TitleStatusHype
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
Testing Probabilistic Circuits—0
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware—0
The Physics of Text: Ontological Realism in Information Extraction—0
The Random Conditional Distribution for Higher-Order Probabilistic Inference—0
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