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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

Showing 7180 of 273 papers

TitleStatusHype
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard ModelCode0
Automatically Marginalized MCMC in Probabilistic ProgrammingCode0
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleCode0
Exploring Bayesian approaches to eQTL mapping through probabilistic programmingCode0
An Introduction to Probabilistic ProgrammingCode0
Amortized Rejection Sampling in Universal Probabilistic ProgrammingCode0
Deep Amortized Inference for Probabilistic ProgramsCode0
Hamiltonian Monte Carlo for Probabilistic Programs with DiscontinuitiesCode0
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric BayesCode0
Compositional Semantics for Probabilistic Programs with Exact ConditioningCode0
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