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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 221–230 of 273 papers

TitleStatusHype
Inference Compilation and Universal Probabilistic ProgrammingCode0
Consistent Kernel Mean Estimation for Functions of Random Variables—0
Deep Amortized Inference for Probabilistic ProgramsCode0
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models—0
Robust Energy Storage Scheduling for Imbalance Reduction of Strategically Formed Energy Balancing Groups—0
Probabilistic Data Analysis with Probabilistic ProgrammingCode0
Practical optimal experiment design with probabilistic programs—0
TerpreT: A Probabilistic Programming Language for Program Induction—0
Automatic Generation of Probabilistic Programming from Time Series Data—0
Swift: Compiled Inference for Probabilistic Programming Languages—0
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