The PILGRIM (ProbabIListic Graphical and RelatIonal Models) platform aims at providing efficient software tools in order to define and learn different probabilistic graphical models (currently directed ones: Bayesian networks and Probabilistic Relational Models), and use them to make inference on data. It is composed of 2 different projects:
- PILGRIM General allows to define, learn and serialize Bayesian networks (including Dynamic Bayesian networks). It offers structure learning algorithms for Bayesian networks (e.g. greedy search, MMHC), structure evaluations (e.g. BDeu, BIC, SHD), and other related statistical tests (e.g. mutual information). It also offers several utility methods such as connecting to tabular datasets, computing common measures like KL-divergence etc.
- PILGRIM Relational offers many features for the definition and learning of Probabilistic Relational Models (PRM) and their extensions, as well as their instantiation into grounded models for inference.
PILGRIM is actively developed and maintained by members of the Data, User, Knowledge (DUKe) team of the CNRS LS2N Laboratory, in Nantes, France, with the help of Ouest Valorisation, Technology transfer acceleration company. The project is led by Pr. Philippe Leray.
The PILGRIM platform comes in two flavors: a GPL 3.0 version, distributed from this website (you can download the latest version here), and an extended version with extra proprietary code. If you are interested in the latter case, please contact us.
The PILGRIM project relies on and encapsulates the ProBT platform, developed by ProbaYes. ProBT allows to model, learn and make inference in Bayesian networks and PILGRIM reuses many of its classes defining basic concepts in probabilistic programming, while extending its capabilities in terms of Bayesian networks structure learning and in terms of relational datasets support.