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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 91–100 of 273 papers

TitleStatusHype
Complex Coordinate-Based Meta-Analysis with Probabilistic Programming—0
Compartmental Models for COVID-19 and Control via Policy Interventions—0
Augur: a Modeling Language for Data-Parallel Probabilistic Inference—0
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching—0
A Step from Probabilistic Programming to Cognitive Architectures—0
A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems—0
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs—0
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation—0
A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors—0
The Mathematics of Changing one's Mind, via Jeffrey's or via Pearl's update rule—0
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