\documentclass[12pt]{article}
\usepackage{amsmath,amssymb,amsfonts}
\begin{document}
Inferring Bayesian network structure from data is a challenging issue and many researchers have been working on this problem. The K2 is a well-known order dependent algorithm to learn Bayesian network. The result of the algorithm is not robust since it achieves different network structure if node orderings are permuted. Consequently, choosing suitable sequential node ordering for the input
of the K2 algorithm is a challenging task. In this work, some deterministic methods for selecting suitable sequential node ordering are introduced. The effectiveness of these methods bench marked through the Asia, Alarm, Car and Insurance networks. The results indicate that the methods based on the concept of mutual information and entropy are suitable for finding a sequential node ordering
and considerably improves the precision of network inference. The source code and selected data sets are available on http://profiles.bs.ipm.ir/softwares/ordering/.
Key words: Inferring Bayesian network structure, the K2 Algorithm, order dependent algorithm, mutual information, entropy.
\end{document}