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.
Full documentation is available at BIASlab website.
It is also possible to build documentation locally. Just execute
$ julia make.jl
docs/ directory to build a local version of the documentation.
Install ForneyLab through the Julia package manager:
] add ForneyLab
If you want to be able to use the graph visualization functions, you will also need to have GraphViz installed. On Linux, just use
apt-get install graphviz or
yum install graphviz. On Windows, run the installer and afterwards manually add the path of the GraphViz installation to the
PATH system variable. On MacOS, use for example
brew install graphviz. The
dot command should work from the command line.
Some demos use the PyPlot plotting module. Install it using
] add PyPlot.
] test ForneyLab to validate the installation by running the test suite.
(c) 2019 GN Store Nord A/S. Permission to use this software for any non-commercial purpose is granted. See
LICENSE.md file for details.