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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 2130 of 273 papers

TitleStatusHype
BayesCard: Revitilizing Bayesian Frameworks for Cardinality EstimationCode1
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic ProgrammingCode1
3DP3: 3D Scene Perception via Probabilistic ProgrammingCode1
Scalable Neural-Probabilistic Answer Set ProgrammingCode1
Scenic: A Language for Scenario Specification and Data GenerationCode1
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming ApproachCode1
Inferring Signaling Pathways with Probabilistic ProgrammingCode1
Conditional independence by typingCode1
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and DeterministicCode1
TreeFlow: probabilistic programming and automatic differentiation for phylogeneticsCode1
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