FactorBase: Learning Graphical Models from multi-relational data. The source code repository for the FactorBase system. The code in this repository implements the learn-and-join algorithm (see algorithm paper on ”Learning Graphical Models for Relational Data via Lattice Search”). Input: A relational schema hosted on a MySQL server. Output: A Bayesian network that shows probabilistic dependencies between the relationships and attributes represented in the database. Both network structure and parameters are computed by the system.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Riahi, Fatemeh; Schulte, Oliver: Model-based exception mining for object-relational data (2020)
- Nguembang Fadja, Arnaud; Riguzzi, Fabrizio: Lifted discriminative learning of probabilistic logic programs (2019)
- Schulte, Oliver; Qian, Zhensong; Kirkpatrick, Arthur E.; Yin, Xiaoqian; Sun, Yan: Fast learning of relational dependency networks (2016)
- Di Mauro, Nicola; Bellodi, Elena; Riguzzi, Fabrizio: Bandit-based Monte-Carlo structure learning of probabilistic logic programs (2015)
- Kimmig, Angelika; Mihalkova, Lilyana; Getoor, Lise: Lifted graphical models: a survey (2015)
- Schulte, Oliver; Khosravi, Hassan; Kirkpatrick, Arthur E.; Gao, Tianxiang; Zhu, Yuke: Modelling relational statistics with Bayes nets (2014)
- Schulte, Oliver; Khosravi, Hassan: Learning graphical models for relational data via lattice search (2012) ioport