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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 151–160 of 273 papers

TitleStatusHype
Statistical Learning of Conjunction Data Messages Through a Bayesian Non-Homogeneous Poisson Process—0
Stochastically Differentiable Probabilistic Programs—0
Strengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming—0
String Diagrams with Factorized Densities—0
Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors—0
Structured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages—0
Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs—0
Summary - TerpreT: A Probabilistic Programming Language for Program Induction—0
Supervised Bayesian Specification Inference from Demonstrations—0
Surrogate Likelihoods for Variational Annealed Importance Sampling—0
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