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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 141–150 of 273 papers

TitleStatusHype
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
Towards an architectural framework for intelligent virtual agents using probabilistic programming—0
Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models—0
Transforming Probabilistic Programs for Model Checking—0
Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models—0
Uncertainty Analysis in SPECT Reconstruction based on Probabilistic Programming—0
Unifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representations—0
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