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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
Mapping probability word problems to executable representations—0
3DP3: 3D Scene Perception via Probabilistic ProgrammingCode1
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
Unifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representations—0
SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation—0
EinSteinVI: General and Integrated Stein Variational Inference—0
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect—0
Proceedings 37th International Conference on Logic Programming (Technical Communications)—0
Addressing the IEEE AV Test Challenge with Scenic and VerifAI—0
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs—0
Pixyz: a Python library for developing deep generative models—0
Unifying incidence and prevalence under a time-varying general branching processCode0
Supervised Bayesian Specification Inference from Demonstrations—0
Nonparametric Hamiltonian Monte CarloCode1
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently—0
How To Train Your Program: a Probabilistic Programming Pattern for Bayesian Learning From Data—0
Probabilistic Programming Bots in Intuitive Physics Game Play—0
D3p -- A Python Package for Differentially-Private Probabilistic ProgrammingCode1
RecSim NG: Toward Principled Uncertainty Modeling for Recommender EcosystemsCode1
Meta-Learning an Inference Algorithm for Probabilistic Programs—0
Compositional Semantics for Probabilistic Programs with Exact ConditioningCode0
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