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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 101–110 of 273 papers

TitleStatusHype
SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation—0
Unifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representations—0
EinSteinVI: General and Integrated Stein Variational Inference—0
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect—0
Proceedings 37th International Conference on Logic Programming (Technical Communications)—0
Addressing the IEEE AV Test Challenge with Scenic and VerifAI—0
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs—0
Pixyz: a Python library for developing deep generative models—0
Unifying incidence and prevalence under a time-varying general branching processCode0
Supervised Bayesian Specification Inference from Demonstrations—0
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