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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 131–140 of 273 papers

TitleStatusHype
Uncertainty Analysis in SPECT Reconstruction based on Probabilistic Programming—0
A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors—0
Formal Analysis and Redesign of a Neural Network-Based Aircraft Taxiing System with VerifAI—0
Stochastically Differentiable Probabilistic Programs—0
Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors—0
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware—0
Automatic structured variational inferenceCode0
Joint Distributions for TensorFlow ProbabilityCode0
Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach—0
Stochastic Probabilistic ProgramsCode0
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