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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
Simple, Distributed, and Accelerated Probabilistic Programming—0
Simulation-based inference methods for particle physics—0
Simulation Intelligence: Towards a New Generation of Scientific Methods—0
SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming—0
Slice Sampling for Probabilistic Programming—0
Representation of Molecules via Algebraic Data Types : Advancing Beyond SMILES & SELFIES—0
SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation—0
Sound Abstraction and Decomposition of Probabilistic Programs—0
Spreadsheet Probabilistic Programming—0
Static Analysis for Probabilistic Programs—0
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