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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 41–50 of 273 papers

TitleStatusHype
A Heavy-Tailed Algebra for Probabilistic Programming—0
A Probabilistic Programming Approach To Probabilistic Data Analysis—0
A Fairness-aware Hybrid Recommender System—0
A Convenient Category for Higher-Order Probability Theory—0
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support—0
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations—0
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs—0
Bayesian causal inference via probabilistic program synthesis—0
Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data—0
BayesDB: A probabilistic programming system for querying the probable implications of data—0
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