Cognitive radio is a new technology that aims to solve the scarcity and underutilization of the radio spectrum. It also aims to achieve its goals with a high quality of service. Hence, channel quality estimation metrics are used to help the cognitive radio to readjust its parameters and enhance its quality of service. One of these metrics is the probability of outage. This metric depends on either the level of signal to interference plus noise ratio (SINR), or the channel capacity and data rate. However, uncertainty affects these two variables, which in turns affects the probability of outage. Therefore, a method that deals with uncertainty is necessary. In this paper, we propose a model based on a Bayesian network. This method qualitatively and quantitatively relates the variables affecting SINR and outage probability by a conditional probabilistic model. The results of the proposed Bayesian model show the effectiveness in handling uncertainty.