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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 71–80 of 273 papers

TitleStatusHype
Anytime Exact Belief Propagation—0
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms—0
Efficient Incremental Belief Updates Using Weighted Virtual Observations—0
Deep Probabilistic Programming—0
Automatic Inference for Inverting Software Simulators via Probabilistic Programming—0
Designing Perceptual Puzzles by Differentiating Probabilistic Programs—0
Declarative Statistical Modeling with Datalog—0
Declarative Probabilistic Logic Programming in Discrete-Continuous Domains—0
Automatic Generation of Probabilistic Programming from Time Series Data—0
Detecting and Quantifying Malicious Activity with Simulation-based Inference—0
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