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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 26–50 of 273 papers

TitleStatusHype
TreeFlow: probabilistic programming and automatic differentiation for phylogeneticsCode1
Scalable Neural-Probabilistic Answer Set ProgrammingCode1
RecSim NG: Toward Principled Uncertainty Modeling for Recommender EcosystemsCode1
Inferring Signaling Pathways with Probabilistic ProgrammingCode1
PPL Bench: Evaluation Framework For Probabilistic Programming LanguagesCode1
3DP3: 3D Scene Perception via Probabilistic ProgrammingCode1
Inference Compilation and Universal Probabilistic ProgrammingCode0
Improved Marginal Unbiased Score Expansion (MUSE) via Implicit DifferentiationCode0
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects ModelsCode0
Hamiltonian Monte Carlo for Probabilistic Programs with DiscontinuitiesCode0
Ice Core Dating using Probabilistic ProgrammingCode0
Joint Distributions for TensorFlow ProbabilityCode0
Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy PredictionCode0
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic ProgrammingCode0
Bayesian Calibration of MEMS AccelerometersCode0
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at ScaleCode0
Exploring Bayesian approaches to eQTL mapping through probabilistic programmingCode0
Functional Tensors for Probabilistic ProgrammingCode0
Applying Probabilistic Programming to Affective ComputingCode0
Differentiable Quantum Programming with Unbounded LoopsCode0
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic ProgramsCode0
A Factor Graph Approach to Automated Design of Bayesian Signal Processing AlgorithmsCode0
A Bayesian Monte Carlo approach for predicting the spread of infectious diseasesCode0
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric BayesCode0
Diffusion models for probabilistic programmingCode0
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