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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 201–225 of 273 papers

TitleStatusHype
Neural Probabilistic Logic Programming in Discrete-Continuous Domains—0
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels—0
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference—0
ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs—0
Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation—0
Particle Gibbs with Ancestor Sampling for Probabilistic Programs—0
Pearl's and Jeffrey's Update as Modes of Learning in Probabilistic Programming—0
COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty—0
Picture: A Probabilistic Programming Language for Scene Perception—0
Pixyz: a Python library for developing deep generative models—0
Practical optimal experiment design with probabilistic programs—0
Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features—0
Probabilistic Planning by Probabilistic Programming—0
Probabilistic Programming Bots in Intuitive Physics Game Play—0
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard ModelCode0
Machine Teaching of Active Sequential LearnersCode0
Modular Deep Probabilistic ProgrammingCode0
Push: Concurrent Probabilistic Programming for Bayesian Deep LearningCode0
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleCode0
Diffusion models for probabilistic programmingCode0
Differentiable Quantum Programming with Unbounded LoopsCode0
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic ProgrammingCode0
Exploring Bayesian approaches to eQTL mapping through probabilistic programmingCode0
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic ProgrammingCode0
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic ProgrammingCode0
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