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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 111–120 of 273 papers

TitleStatusHype
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently—0
How To Train Your Program: a Probabilistic Programming Pattern for Bayesian Learning From Data—0
Probabilistic Programming Bots in Intuitive Physics Game Play—0
Meta-Learning an Inference Algorithm for Probabilistic Programs—0
Compositional Semantics for Probabilistic Programs with Exact ConditioningCode0
Einstein VI: General and Integrated Stein Variational Inference in NumPyro—0
Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation—0
Complex Coordinate-Based Meta-Analysis with Probabilistic Programming—0
Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models—0
Survival prediction and risk estimation of Glioma patients using mRNA expressions—0
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