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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 176–200 of 273 papers

TitleStatusHype
Effect Handling for Composable Program Transformations in Edward2—0
A Factor Graph Approach to Automated Design of Bayesian Signal Processing AlgorithmsCode0
Simple, Distributed, and Accelerated Probabilistic ProgrammingCode0
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and DeterministicCode1
Pyro: Deep Universal Probabilistic ProgrammingCode0
Automated learning with a probabilistic programming language: Birch—0
Sinkhorn AutoEncodersCode0
Inference Over Programs That Make Predictions—0
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic ProgrammingCode0
Neural Distribution Learning for generalized time-to-event prediction—0
An Introduction to Probabilistic ProgrammingCode0
Scenic: A Language for Scenario Specification and Scene GenerationCode1
A Fairness-aware Hybrid Recommender System—0
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
Probabilistic Planning by Probabilistic Programming—0
Bayesian Neural NetworksCode0
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