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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 101–125 of 273 papers

TitleStatusHype
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
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
Meta-Learning an Inference Algorithm for Probabilistic Programs—0
Compositional Semantics for Probabilistic Programs with Exact ConditioningCode0
Einstein VI: General and Integrated Stein Variational Inference in NumPyro—0
Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation—0
Complex Coordinate-Based Meta-Analysis with Probabilistic Programming—0
Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models—0
Survival prediction and risk estimation of Glioma patients using mRNA expressions—0
Recalibrating classifiers for interpretable abusive content detection—0
BayCANN: Streamlining Bayesian Calibration with Artificial Neural Network Metamodeling—0
Accelerating Metropolis-Hastings with Lightweight Inference CompilationCode0
Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy PredictionCode0
Simulation-based inference methods for particle physics—0
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