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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
TensorFlow DistributionsCode2
Language Model CascadesCode2
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of ThoughtCode2
DynamicPPL: Stan-like Speed for Dynamic Probabilistic ModelsCode1
Spacecraft Collision Risk Assessment with Probabilistic ProgrammingCode1
RecSim NG: Toward Principled Uncertainty Modeling for Recommender EcosystemsCode1
TreeFlow: probabilistic programming and automatic differentiation for phylogeneticsCode1
Sequential Monte Carlo Steering of Large Language Models using Probabilistic ProgramsCode1
Scaling Integer Arithmetic in Probabilistic ProgramsCode1
Nonparametric Hamiltonian Monte CarloCode1
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming ApproachCode1
πVAE: a stochastic process prior for Bayesian deep learning with MCMCCode1
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic ProgrammingCode1
Inferring Signaling Pathways with Probabilistic ProgrammingCode1
Planning as Inference in Epidemiological ModelsCode1
Scalable Neural-Probabilistic Answer Set ProgrammingCode1
Scenic: A Language for Scenario Specification and Scene GenerationCode1
Simplifying debiased inference via automatic differentiation and probabilistic programmingCode1
BayesCard: Revitilizing Bayesian Frameworks for Cardinality EstimationCode1
SPPL: Probabilistic Programming with Fast Exact Symbolic InferenceCode1
3DP3: 3D Scene Perception via Probabilistic ProgrammingCode1
D3p -- A Python Package for Differentially-Private Probabilistic ProgrammingCode1
Scenic: A Language for Scenario Specification and Data GenerationCode1
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and DeterministicCode1
Nonparametric Involutive Markov Chain Monte CarloCode1
Automatic Differentiation Variational InferenceCode1
PPL Bench: Evaluation Framework For Probabilistic Programming LanguagesCode1
Conditional independence by typingCode1
Augur: a Modeling Language for Data-Parallel Probabilistic Inference—0
A Step from Probabilistic Programming to Cognitive Architectures—0
A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems—0
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation—0
A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors—0
The Mathematics of Changing one's Mind, via Jeffrey's or via Pearl's update rule—0
Addressing the IEEE AV Test Challenge with Scenic and VerifAI—0
A Programmatic and Semantic Approach to Explaining and DebuggingNeural Network Based Object Detectors—0
A Probabilistic Programming Idiom for Active Knowledge Search—0
A Heavy-Tailed Algebra for Probabilistic Programming—0
A Probabilistic Programming Approach To Probabilistic Data Analysis—0
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs—0
A Fairness-aware Hybrid Recommender System—0
A Convenient Category for Higher-Order Probability Theory—0
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support—0
BayesDB: A probabilistic programming system for querying the probable implications of data—0
Bayesian causal inference via probabilistic program synthesis—0
Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data—0
A Compilation Target for Probabilistic Programming Languages—0
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