GraphPPL.jl: A Probabilistic Programming Language for Graphical Models.

This paper presents GraphPPL.jl, a novel probabilistic programming language designed for graphical models. GraphPPL.

Automating Model Comparison in Factor Graphs.

Bayesian state and parameter estimation are automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often overlooked despite its importance. This paper efficiently automates Bayesian model averaging, selection, and combination by message passing on a Forney-style factor graph with a custom mixture node.