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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 121–130 of 273 papers

TitleStatusHype
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
Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program AnalysisCode0
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
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