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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 241–250 of 273 papers

TitleStatusHype
Deployable probabilistic programming—0
Designing Perceptual Puzzles by Differentiating Probabilistic Programs—0
Detecting and Quantifying Malicious Activity with Simulation-based Inference—0
Detecting Parameter Symmetries in Probabilistic Models—0
Dimensionality Reduction as Probabilistic Inference—0
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms—0
Doubly Bayesian Optimization—0
Effect Handling for Composable Program Transformations in Edward2—0
Efficient Incremental Belief Updates Using Weighted Virtual Observations—0
Efficient Inference Amortization in Graphical Models using Structured Continuous Conditional Normalizing Flows—0
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