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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 31–40 of 273 papers

TitleStatusHype
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
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