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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

Showing 71–80 of 273 papers

TitleStatusHype
Differentiable Quantum Programming with Unbounded LoopsCode0
Ice Core Dating using Probabilistic ProgrammingCode0
Improved Marginal Unbiased Score Expansion (MUSE) via Implicit DifferentiationCode0
Robust leave-one-out cross-validation for high-dimensional Bayesian modelsCode0
Borch: A Deep Universal Probabilistic Programming LanguageCode0
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development—0
Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming—0
Multi-Model Probabilistic Programming—0
Proceedings 38th International Conference on Logic Programming—0
Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models—0
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