%0 Journal Article %1 michail2018bayesian %A Tsagris, Michail %D 2018 %J Applied Artificial Intelligence %K mxmcausalpath %N 2 %P 101-123 %R 10.1080/08839514.2018.1526760 %T Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation %U https://www.researchgate.net/profile/Michail_Tsagris/publication/327884019_Bayesian_Network_Learning_with_the_PC_Algorithm_An_Improved_and_Correct_Variation/links/5bab44c945851574f7e65688/Bayesian-Network-Learning-with-the-PC-Algorithm-An-Improved-and-Correct-Variation.pdf %V 33 %X PC is a prototypical constraint-based algorithm for learning Bayesian networks, a special case of directed acyclic graphs. An existing variant of it, in the R package pcalg, was developed to make the skeleton phase order independent. In return, it has notably increased execution time. In this paper, we clarify that the PC algorithm the skeleton phase of PC is indeed order independent. The modification we propose outperforms pcalg’s variant of the PC in terms of returning correct networks of better quality as is less prone to errors and in some cases it is a lot more computationally cheaper. In addition, we show that pcalg’s variant does not return valid acyclic graphs.

@article{michail2018bayesian, abstract = {PC is a prototypical constraint-based algorithm for learning Bayesian networks, a special case of directed acyclic graphs. An existing variant of it, in the R package pcalg, was developed to make the skeleton phase order independent. In return, it has notably increased execution time. In this paper, we clarify that the PC algorithm the skeleton phase of PC is indeed order independent. The modification we propose outperforms pcalg’s variant of the PC in terms of returning correct networks of better quality as is less prone to errors and in some cases it is a lot more computationally cheaper. In addition, we show that pcalg’s variant does not return valid acyclic graphs.}, added-at = {2019-02-01T12:44:24.000+0100}, author = {Tsagris, Michail}, biburl = {https://www.bibsonomy.org/bibtex/2af15fddd4692e8ba7df0f00f5de6fd23/mensxmachina}, doi = {10.1080/08839514.2018.1526760}, interhash = {6c22e7e09e9aa7a24536cef5b953528e}, intrahash = {af15fddd4692e8ba7df0f00f5de6fd23}, journal = {Applied Artificial Intelligence }, keywords = {mxmcausalpath}, number = 2, pages = {101-123}, timestamp = {2021-03-10T09:26:33.000+0100}, title = {Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation}, url = {https://www.researchgate.net/profile/Michail_Tsagris/publication/327884019_Bayesian_Network_Learning_with_the_PC_Algorithm_An_Improved_and_Correct_Variation/links/5bab44c945851574f7e65688/Bayesian-Network-Learning-with-the-PC-Algorithm-An-Improved-and-Correct-Variation.pdf}, volume = 33, year = 2018 }