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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
BlackJAX: Composable Bayesian inference in JAXCode5
SymbolicAI: A framework for logic-based approaches combining generative models and solversCode5
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparisonCode2
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of ThoughtCode2
Language Model CascadesCode2
TensorFlow DistributionsCode2
Simplifying debiased inference via automatic differentiation and probabilistic programmingCode1
Scaling Integer Arithmetic in Probabilistic ProgramsCode1
Scalable Neural-Probabilistic Answer Set ProgrammingCode1
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
TreeFlow: probabilistic programming and automatic differentiation for phylogeneticsCode1
Nonparametric Involutive Markov Chain Monte CarloCode1
3DP3: 3D Scene Perception via Probabilistic ProgrammingCode1
Nonparametric Hamiltonian Monte CarloCode1
D3p -- A Python Package for Differentially-Private Probabilistic ProgrammingCode1
RecSim NG: Toward Principled Uncertainty Modeling for Recommender EcosystemsCode1
BayesCard: Revitilizing Bayesian Frameworks for Cardinality EstimationCode1
Spacecraft Collision Risk Assessment with Probabilistic ProgrammingCode1
Conditional independence by typingCode1
PPL Bench: Evaluation Framework For Probabilistic Programming LanguagesCode1
Scenic: A Language for Scenario Specification and Data GenerationCode1
SPPL: Probabilistic Programming with Fast Exact Symbolic InferenceCode1
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic ProgrammingCode1
Inferring Signaling Pathways with Probabilistic ProgrammingCode1
Planning as Inference in Epidemiological ModelsCode1
πVAE: a stochastic process prior for Bayesian deep learning with MCMCCode1
DynamicPPL: Stan-like Speed for Dynamic Probabilistic ModelsCode1
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and DeterministicCode1
Scenic: A Language for Scenario Specification and Scene GenerationCode1
Automatic Differentiation Variational InferenceCode1
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
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
Efficient Incremental Belief Updates Using Weighted Virtual Observations—0
Statistical Learning of Conjunction Data Messages Through a Bayesian Non-Homogeneous Poisson Process—0
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