Program to find a missing sailor at sea using Bayes theorem

A sailor is lost at sea, human life is in danger, and the search efforts of the rescue team has to be as precise as possible, luckily you as the captain of the search team has a powerful tool in your arsenal to make smarter decisions on your rescue efforts- Bayes Theorem

The Logic You've narrowed the location of a missing sailor to be one amongst 3 search areas, this is gotten from a SAROPS software (Search and Rescue Optimal Planning System (SAROPS) is a comprehensive search and rescue (SAR) planning system used by the United States Coast Guard which estimates the missing sailor's possible locations)

The missing sailor's location is randomly set on the map "cape_python.png". Our program provides initial probability to the sailor's location and allows a user to search for the missing sailor using these probabilities, the probabilities of finding the sailor in each search area are updated using Bayes theorem. Allowing the user(which in this case is the rescue team) to perform a more scientific search for this missing sailor rather than simply searching by guessing.

Who wins in this battle, The Cold logic of Bayes theorem or "trusting your Gut"?