> For the complete documentation index, see [llms.txt](https://alfredo-reyes-montero.gitbook.io/gen/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://alfredo-reyes-montero.gitbook.io/gen/julia/packages/forneylab.md).

# ForneyLab

[ForneyLab.jl](https://github.com/biaslab/ForneyLab.jl) is a Julia package for automatic generation of (Bayesian) inference algorithms. Given a probabilistic model, ForneyLab generates efficient Julia code for message-passing based inference. It uses the model structure to generate an algorithm that consists of a sequence of local computations on a Forney-style factor graph (FFG) representation of the model. For an excellent introduction to message passing and FFGs, see [The Factor Graph Approach to Model-Based Signal Processing](https://ieeexplore.ieee.org/document/4282128/) by Loeliger et al. (2007). Moreover, for a comprehensive overview of the underlying principles behind this tool, see [A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms](https://arxiv.org/abs/1811.03407) by Cox et. al. (2018).

We designed ForneyLab with a focus on flexible and modular modeling of time-series data. ForneyLab enables a user to:

* Conveniently specify a probabilistic model;
* Automatically generate an efficient inference algorithm;
* Compile the inference algorithm to executable Julia code.