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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
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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