Bayesian inference with probabilistic programming.
-
Updated
Jun 9, 2024 - Julia
Bayesian inference with probabilistic programming.
A domain-specific probabilistic programming language for scalable Bayesian data cleaning
Julia package for automatically generating Bayesian inference algorithms through message passing on Forney-style factor graphs.
High-performance reactive message-passing based Bayesian inference engine
Inference of microbial interaction networks from large-scale heterogeneous abundance data
Bayesian inference on wiring diagrams.
Clustering via Dirichlet Process Mixture Models
Factor potentials for factor graphs, Bayesian networks, and Markov random fields
Source code for the paper "Lifting Factor Graphs with Some Unknown Factors" (ECSQARU 2023)
Source code for the paper "Colour Passing Revisited: Lifted Model Construction with Commutative Factors" (AAAI 2024)
Source code for the paper "Lifted Causal Inference in Relational Domains" (CLeaR 2024)
Source code for the paper "Efficient Detection of Exchangeable Factors in Factor Graphs" (FLAIRS 2024)
Type stable implementation of a Bayesian network.
Add a description, image, and links to the probabilistic-graphical-models topic page so that developers can more easily learn about it.
To associate your repository with the probabilistic-graphical-models topic, visit your repo's landing page and select "manage topics."