Search and rescue operations demand a fast and reliable process for locating victims. The use of unmanned aerial vehicles (UAV s) has become a prominent research topic due to their lower cost and operational flexibility compared to manned air-craft. This study investigates the use of Bayesian probabilistic methods to estimate the sensitivity of a drone swarm in maritime search and rescue missions. An initial sensitivity distribution is defined based on both prior exact and approximate knowledge, and is subsequently updated using observational data: the number of drones (out of 10) that successfully identified a survivor while flying over the same area. The model is iteratively updated and evaluated. Results show that even after just four missions, there is a consistent convergence of the mean and a reduction in the standard deviation from 0.1307 to 0.0449. These findings demonstrate a reusable framework that improves swarm sensitivity estimation even with limited data. The full code is available at https://github.com/lima0luciano/bayesian-drone-sar.