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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 251–260 of 273 papers

TitleStatusHype
Efficient Search-Based Weighted Model Integration—0
Einstein VI: General and Integrated Stein Variational Inference in NumPyro—0
EinSteinVI: General and Integrated Stein Variational Inference—0
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently—0
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs—0
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing—0
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
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect—0
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