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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 171–180 of 273 papers

TitleStatusHype
Efficient Search-Based Weighted Model Integration—0
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable ModelsCode0
Tensor Variable Elimination for Plated Factor Graphs—0
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming LanguageCode0
Doubly Bayesian Optimization—0
Bayesian Layers: A Module for Neural Network Uncertainty—0
Nested Reasoning About Autonomous Agents Using Probabilistic Programs—0
Bayesian Inference of Temporal Task Specifications from Demonstrations—0
Joint Mapping and Calibration via Differentiable Sensor Fusion—0
Effect Handling for Composable Program Transformations in Edward2—0
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