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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 1–25 of 273 papers

TitleStatusHype
Large Language Bayes—0
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Proceedings 40th International Conference on Logic Programming—0
Representation of Molecules via Algebraic Data Types : Advancing Beyond SMILES & SELFIES—0
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects ModelsCode0
A Distribution Semantics for Probabilistic Term Rewriting—0
Identifying latent disease factors differently expressed in patient subgroups using group factor analysis—0
Inference Plans for Hybrid Particle Filtering—0
Markov Senior -- Learning Markov Junior Grammars to Generate User-specified ContentCode0
Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features—0
Querying Labeled Time Series Data with Scenario Programs—0
Probabilistic Programming with Programmable Variational Inference—0
Data Petri Nets meet Probabilistic Programming (Extended version)—0
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparisonCode2
Simplifying debiased inference via automatic differentiation and probabilistic programmingCode1
ScenicNL: Generating Probabilistic Scenario Programs from Natural Language—0
COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty—0
Automated Efficient Estimation using Monte Carlo Efficient Influence Functions—0
BlackJAX: Composable Bayesian inference in JAXCode5
Efficient Incremental Belief Updates Using Weighted Virtual Observations—0
SymbolicAI: A framework for logic-based approaches combining generative models and solversCode5
Statistical Learning of Conjunction Data Messages Through a Bayesian Non-Homogeneous Poisson Process—0
Diffusion models for probabilistic programmingCode0
Worst-Case Analysis is Maximum-A-Posteriori Estimation—0
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