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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 191–200 of 273 papers

TitleStatusHype
Machine Teaching of Active Sequential LearnersCode0
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard ModelCode0
The Mathematics of Changing one's Mind, via Jeffrey's or via Pearl's update rule—0
Sound Abstraction and Decomposition of Probabilistic Programs—0
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms—0
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic ProgrammingCode0
Deep Probabilistic Programming Languages: A Qualitative Study—0
Hamiltonian Monte Carlo for Probabilistic Programs with DiscontinuitiesCode0
Nesting Probabilistic Programs—0
Modelling contextuality by probabilistic programs with hypergraph semantics—0
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