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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 126–150 of 273 papers

TitleStatusHype
Probabilistic Programming with Programmable Variational Inference—0
Probabilistic unifying relations for modelling epistemic and aleatoric uncertainty: semantics and automated reasoning with theorem proving—0
Proceedings 37th International Conference on Logic Programming (Technical Communications)—0
Proceedings 38th International Conference on Logic Programming—0
Proceedings 40th International Conference on Logic Programming—0
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
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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