%0 Journal Article %1 Bouckaert2007 %A Bouckaert, Remco R. %A Studený, Milan %D 2007 %J Int. J. Approx. Reasoning %K imported %N 2 %P 386-401 %T Racing algorithms for conditional independence inference %V 45 %X In this article, we consider the computational aspects of deciding whether a conditional independence statement $t$ is implied by a list of conditional independence statements $L$ using the implication related to the method of structural imsets. We present two methods which have the interesting complementary properties that one method performs well to prove that $t$ is implied by $L$, while the other performs well to prove that $t$ is not implied by $L$. However, both methods do not well perform the opposite. This gives rise to a parallel algorithm in which both methods race against each other in order to determine effectively whether $t$ is or is not implied. Some empirical evidence is provided that suggest this racing algorithms method performs considerably better than an existing method based on so-called skeletal characterization of the respective implication. Furthermore, unlike previous methods, the method is able to handle more than five variables.

@article{Bouckaert2007, __markedentry = {[joe]}, abstract = {In this article, we consider the computational aspects of deciding whether a conditional independence statement $t$ is implied by a list of conditional independence statements $L$ using the implication related to the method of structural imsets. We present two methods which have the interesting complementary properties that one method performs well to prove that $t$ is implied by $L$, while the other performs well to prove that $t$ is not implied by $L$. However, both methods do not well perform the opposite. This gives rise to a parallel algorithm in which both methods race against each other in order to determine effectively whether $t$ is or is not implied. Some empirical evidence is provided that suggest this racing algorithms method performs considerably better than an existing method based on so-called skeletal characterization of the respective implication. Furthermore, unlike previous methods, the method is able to handle more than five variables.}, added-at = {2010-06-23T08:39:21.000+0200}, author = {Bouckaert, Remco R. and Studen{\'y}, Milan}, biburl = {https://www.bibsonomy.org/bibtex/272abba52b7f1d13d22cf6a4244a0bf0d/joelotz}, interhash = {b60116fa01559f3f165c8b92dc21f259}, intrahash = {72abba52b7f1d13d22cf6a4244a0bf0d}, journal = {Int. J. Approx. Reasoning}, keywords = {imported}, number = 2, owner = {joe}, pages = {386-401}, pdf = {http://www.cs.waikato.ac.nz/~ml/publications/2007/racing2.pdf}, timestamp = {2010-06-23T08:39:21.000+0200}, title = {Racing algorithms for conditional independence inference}, volume = 45, year = 2007 }