A hierarchical Bayes model for analysis of contingency Tables is presented and, using it, inference about correlation parameter, logarithm of the odds ratio, is studied. For drawing random sample from the distribution of logarithm of the odds ratio, computational method of Gibbs sampling is used. For testing independence the use of the hierarchical Bayes in calculating the Bayes factor is introduced and in an applied example is employed.

Software defined networks have attracted enormous attention because they simplify the process of setting up the network. They have been able to leave behind in a short time, most of the technologies that were used in traditional networks by industrialists and researchers. The ease and efficiency are due to the separation of control and data planes from each other such that the control plane is a logically centralized controller and the data plane switches in as the Flow table has been implemented. In these networks, network topology adjustment is done using a Flow table that has special Flow rules and network services, such as Qos, security and etc., which operate as programs on the network. Flow Tables can be directly or indirectly changed by any of these services in the network. Although access to the table of current units simplifies network configurations, it could lead to anomalies between Flow rules for separate modules. In addition to consuming too much switch memory, these anomalies can cause problems for network security and applications. Fortunately, so far, some researches on the detection of anomalies in Flow Tables of software defined networks has been done but this method is not only imposing a great deal of time and processing on the controller, in some cases only conflict resolution has been performed. In this paper we have shown how to speed up the detection algorithm and then tried to improve the speed of anomaly detection algorithm in the Flow table of switches in the software defined networks using different variables.