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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.

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Papers

Showing 261270 of 273 papers

TitleStatusHype
Better call Saul: Flexible Programming for Learning and Inference in NLPCode0
Automatic Reparameterisation of Probabilistic ProgramsCode0
Unifying incidence and prevalence under a time-varying general branching processCode0
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable ModelsCode0
Probabilistic Programs with Stochastic ConditioningCode0
Automatically Marginalized MCMC in Probabilistic ProgrammingCode0
Markov Senior -- Learning Markov Junior Grammars to Generate User-specified ContentCode0
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric BayesCode0
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming LanguageCode0
Accelerating Metropolis-Hastings with Lightweight Inference CompilationCode0
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