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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 131–140 of 273 papers

TitleStatusHype
Program Analysis of Probabilistic Programs—0
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models—0
Querying Labeled Time Series Data with Scenario Programs—0
Querying Labelled Data with Scenario Programs for Sim-to-Real Validation—0
RankPL: A Qualitative Probabilistic Programming Language—0
Recalibrating classifiers for interpretable abusive content detection—0
RELLY: Inferring Hypernym Relationships Between Relational Phrases—0
Robust Energy Storage Scheduling for Imbalance Reduction of Strategically Formed Energy Balancing Groups—0
Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach—0
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints—0
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