Summary In wireless communications, the signal‐to‐interference‐plus‐noise ratio (SINR) is important in spectrum management and link scheduling. In cognitive radio and ad hoc networks, where the spectrum is shared between nodes, the SINR is required to measure the outage probability and the level of accumulated interference on a specific node from other nodes sharing the same band. Several techniques have been proposed to estimate and statistically model the SINR. However, most of these techniques do not account for uncertainty in factors such as the number of nodes and their locations, the distance between nodes, the transmission powers, and the frequencies. In addition, these methods are not able to learn from and adapt to the changes of the network. Therefore, there is a need for models able to dynamically deal with the uncertainty affecting the SINR and provide a modular framework for its estimation. In this article, a Bayesian model is proposed to probabilistically model the SINR and describe how variables affect its probability distribution. Simulation results confirm the validity and robustness of the proposed method.