(eng): This paper describes a modification of the greedy equivalence search (GES) algorithm. The presented modification is based on the algebraic approach to learning. The states of the search space are standard imsets. Each standard imset represents an equivalence class of Bayesian networks. For a given quality criterion the database is represented by the respective data imset. This allows a very simple update of a given quality criterion since the moves between states are represented by differential imsets. We exploit a direct characterization of lower and upper inclusion neighborhood, which allows an efficient search for the best structure in the inclusion neighborhood. The algorithm was implemented in R and is freely available.

(cze): Článek popisuje implementaci hladového algoritmu pro učení baysovských sítí. Algoritmus je založen na algebraických objektech - tzv. imsetech a na prohledávání tzv. inkluzivního okolí.