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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.

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Papers

Showing 5160 of 273 papers

TitleStatusHype
Nonparametric Involutive Markov Chain Monte CarloCode1
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 Development0
Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming0
Multi-Model Probabilistic Programming0
Proceedings 38th International Conference on Logic Programming0
Language Model CascadesCode2
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