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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.

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Papers

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Showing 1–50 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
Inferring Capabilities from Task Performance with Bayesian Triangulation—0
Pearl's and Jeffrey's Update as Modes of Learning in Probabilistic Programming—0
From Probabilistic Programming to Complexity-based Programming—0
Scaling Integer Arithmetic in Probabilistic ProgramsCode1
Towards an architectural framework for intelligent virtual agents using probabilistic programming—0
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of ThoughtCode2
A Heavy-Tailed Algebra for Probabilistic Programming—0
Scalable Neural-Probabilistic Answer Set ProgrammingCode1
Push: Concurrent Probabilistic Programming for Bayesian Deep LearningCode0
Bayesian Calibration of MEMS AccelerometersCode0
Automating Model Comparison in Factor GraphsCode0
Sequential Monte Carlo Steering of Large Language Models using Probabilistic ProgramsCode1
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming ApproachCode1
String Diagrams with Factorized Densities—0
Dimensionality Reduction as Probabilistic Inference—0
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black BoxCode0
Probabilistic unifying relations for modelling epistemic and aleatoric uncertainty: semantics and automated reasoning with theorem proving—0
Neural Probabilistic Logic Programming in Discrete-Continuous Domains—0
Declarative Probabilistic Logic Programming in Discrete-Continuous Domains—0
ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs—0
Incorporating Expert Opinion on Observable Quantities into Statistical Models -- A General Framework—0
Automatically Marginalized MCMC in Probabilistic ProgrammingCode0
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing—0
TreeFlow: probabilistic programming and automatic differentiation for phylogeneticsCode1
Differentiable Quantum Programming with Unbounded LoopsCode0
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