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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 176–200 of 273 papers

TitleStatusHype
Unifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representations—0
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs—0
Using probabilistic programs as proposals—0
Venture: a higher-order probabilistic programming platform with programmable inference—0
Weighted Programming—0
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development—0
WOLFE: An NLP-friendly Declarative Machine Learning Stack—0
Worst-Case Analysis is Maximum-A-Posteriori Estimation—0
Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation—0
A Compilation Target for Probabilistic Programming Languages—0
A Convenient Category for Higher-Order Probability Theory—0
Addressing the IEEE AV Test Challenge with Scenic and VerifAI—0
A Distribution Semantics for Probabilistic Term Rewriting—0
Adversarial Message Passing For Graphical Models—0
A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs—0
A Fairness-aware Hybrid Recommender System—0
A Heavy-Tailed Algebra for Probabilistic Programming—0
The Mathematics of Changing one's Mind, via Jeffrey's or via Pearl's update rule—0
A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems—0
A New Approach to Probabilistic Programming Inference—0
Anytime Exact Belief Propagation—0
Applications of Probabilistic Programming (Master's thesis, 2015)—0
A theory of contemplation—0
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs—0
A Probabilistic Programming Approach To Probabilistic Data Analysis—0
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