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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 201–210 of 273 papers

TitleStatusHype
Neural Probabilistic Logic Programming in Discrete-Continuous Domains—0
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels—0
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference—0
ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs—0
Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation—0
Particle Gibbs with Ancestor Sampling for Probabilistic Programs—0
Pearl's and Jeffrey's Update as Modes of Learning in Probabilistic Programming—0
COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty—0
Picture: A Probabilistic Programming Language for Scene Perception—0
Pixyz: a Python library for developing deep generative models—0
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