ForneyLab

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 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 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.