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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

Showing 201210 of 273 papers

TitleStatusHype
Using probabilistic programs as proposals0
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators0
TensorFlow DistributionsCode2
High Five: Improving Gesture Recognition by Embracing Uncertainty0
ZhuSuan: A Library for Bayesian Deep LearningCode0
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic ProgramsCode0
Anytime Exact Belief Propagation0
RankPL: A Qualitative Probabilistic Programming Language0
Learning Probabilistic Programs Using Backpropagation0
Importance Sampled Stochastic Optimization for Variational Inference0
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