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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 231–240 of 273 papers

TitleStatusHype
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric BayesCode0
Functional Tensors for Probabilistic ProgrammingCode0
Dataflow Matrix Machines as a Generalization of Recurrent Neural NetworksCode0
Compositional Semantics for Probabilistic Programs with Exact ConditioningCode0
Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program AnalysisCode0
A Factor Graph Approach to Automated Design of Bayesian Signal Processing AlgorithmsCode0
Hamiltonian Monte Carlo for Probabilistic Programs with DiscontinuitiesCode0
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects ModelsCode0
Bayesian Neural NetworksCode0
Parameter elimination in particle Gibbs samplingCode0
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