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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 251–273 of 273 papers

TitleStatusHype
WOLFE: An NLP-friendly Declarative Machine Learning Stack—0
Picture: A Probabilistic Programming Language for Scene Perception—0
Automatic Inference for Inverting Software Simulators via Probabilistic Programming—0
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic—0
Particle Gibbs with Ancestor Sampling for Probabilistic Programs—0
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations—0
Slice Sampling for Probabilistic Programming—0
Declarative Statistical Modeling with Datalog—0
Augur: Data-Parallel Probabilistic Modeling—0
Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs—0
Dependency Parsing for Weibo: An Efficient Probabilistic Logic Programming Approach—0
Decision-Making with Complex Data Structures using Probabilistic Programming—0
Learning Probabilistic Programs—0
Venture: a higher-order probabilistic programming platform with programmable inference—0
A Compilation Target for Probabilistic Programming Languages—0
Detecting Parameter Symmetries in Probabilistic Models—0
Augur: a Modeling Language for Data-Parallel Probabilistic Inference—0
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs—0
Declarative Modeling and Bayesian Inference of Dark Matter Halos—0
Automated Variational Inference in Probabilistic Programming—0
A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs—0
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference—0
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs—0
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