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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 21–30 of 273 papers

TitleStatusHype
Efficient Incremental Belief Updates Using Weighted Virtual Observations—0
SymbolicAI: A framework for logic-based approaches combining generative models and solversCode5
Statistical Learning of Conjunction Data Messages Through a Bayesian Non-Homogeneous Poisson Process—0
Diffusion models for probabilistic programmingCode0
Worst-Case Analysis is Maximum-A-Posteriori Estimation—0
Inferring Capabilities from Task Performance with Bayesian Triangulation—0
Pearl's and Jeffrey's Update as Modes of Learning in Probabilistic Programming—0
From Probabilistic Programming to Complexity-based Programming—0
Scaling Integer Arithmetic in Probabilistic ProgramsCode1
Towards an architectural framework for intelligent virtual agents using probabilistic programming—0
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