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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
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
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI—0
Planning as Inference in Epidemiological ModelsCode1
Stochastically Differentiable Probabilistic Programs—0
Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors—0
πVAE: a stochastic process prior for Bayesian deep learning with MCMCCode1
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