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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 76–100 of 273 papers

TitleStatusHype
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development—0
Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming—0
Multi-Model Probabilistic Programming—0
Proceedings 38th International Conference on Logic Programming—0
Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models—0
Tensor Program Optimization with Probabilistic Programs—0
Designing Perceptual Puzzles by Differentiating Probabilistic Programs—0
Program Analysis of Probabilistic Programs—0
Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming—0
A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems—0
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives—0
Compartmental Models for COVID-19 and Control via Policy Interventions—0
A Probabilistic Programming Idiom for Active Knowledge Search—0
Weighted Programming—0
Mixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminism—0
Surrogate Likelihoods for Variational Annealed Importance Sampling—0
Simulation Intelligence: Towards a New Generation of Scientific Methods—0
Querying Labelled Data with Scenario Programs for Sim-to-Real Validation—0
Testing Probabilistic Circuits—0
Mapping probability word problems to executable representations—0
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation—0
flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs—0
LazyPPL: laziness and types in non-parametric probabilistic programs—0
SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming—0
Detecting and Quantifying Malicious Activity with Simulation-based Inference—0
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