GraphPPL.jl: A Probabilistic Programming Language for Graphical Models

GraphPPL.jl: A Probabilistic Programming Language for Graphical Models

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Cloth Dust Bag This paper presents GraphPPL.jl, a novel probabilistic programming language designed for graphical models.GraphPPL.jl uniquely represents probabilistic models as factor graphs.

A notable feature of GraphPPL.jl is its model nesting capability, which facilitates the creation of modular graphical models and significantly simplifies the development of large (hierarchical) graphical models.Furthermore, GraphPPL.jl offers a plugin system to incorporate inference-specific information into the graph, allowing integration with various well-known inference engines.

To demonstrate this, GraphPPL.jl includes a flexible plugin to define a Constrained Bethe Free Energy minimization process, also known as variational inference.In particular, the Constrained Bethe Free Energy defined by GraphPPL.jl serves as a potential inference framework for numerous well-known inference backends, making it a versatile tool for diverse applications.

This paper details the design and implementation of GraphPPL.jl, highlighting its power, expressiveness, and user-friendliness.It also emphasizes the clear separation between model definition and inference while providing developers with extensibility and customization options.This establishes GraphPPL.

jl as a high-level user interface language that allows users to create complex graphical models without being burdened CHEST RUB/STOPITCOLD with the complexity of inference while allowing backend developers to easily adopt GraphPPL.jl as their frontend language.