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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
Einstein VI: General and Integrated Stein Variational Inference in NumPyro—0
BayesCard: Revitilizing Bayesian Frameworks for Cardinality EstimationCode1
Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation—0
Spacecraft Collision Risk Assessment with Probabilistic ProgrammingCode1
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
Conditional independence by typingCode1
Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy PredictionCode0
PPL Bench: Evaluation Framework For Probabilistic Programming LanguagesCode1
Scenic: A Language for Scenario Specification and Data GenerationCode1
Simulation-based inference methods for particle physics—0
Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program AnalysisCode0
SPPL: Probabilistic Programming with Fast Exact Symbolic InferenceCode1
Bayesian Policy Search for Stochastic Domains—0
Probabilistic Programs with Stochastic ConditioningCode0
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels—0
Transforming Probabilistic Programs for Model Checking—0
Uncertainty Analysis in SPECT Reconstruction based on Probabilistic Programming—0
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic ProgrammingCode1
A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors—0
Inferring Signaling Pathways with Probabilistic ProgrammingCode1
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