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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
flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs—0
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI—0
From Probabilistic Programming to Complexity-based Programming—0
Declarative Modeling and Bayesian Inference of Dark Matter Halos—0
Decision-Making with Complex Data Structures using Probabilistic Programming—0
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect—0
ScenicNL: Generating Probabilistic Scenario Programs from Natural Language—0
Graph Tracking in Dynamic Probabilistic Programs via Source Transformations—0
Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming—0
Data Petri Nets meet Probabilistic Programming (Extended version)—0
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