Available at: https://digitalcommons.calpoly.edu/theses/2797
Date of Award
6-2024
Degree Name
MS in Statistics
Department/Program
Statistics
College
College of Science and Mathematics
Advisor
Hunter Glanz
Advisor Department
Statistics
Advisor College
College of Science and Mathematics
Abstract
People who are considered missing have much higher probabilities of being found dead compared to those who are not considered missing in terms of Search and Rescue (SAR) missions. Dementia patients are incredibly likely to be declared missing, and in fact after removing those with dementia the probability of the mission being regarded as missing person case is only about 10%. Additionally, those who go missing are much more likely to be on private land than on protected areas such as forests and parks. These and similar associations can be represented and investigated using a Bayesian network that has been trained on Search and Rescue mission data. By finding associations between factors that affect these missions, SAR teams can find patterns in historical cases and apply them to future cases in order to narrow down their search areas, improve their plans, and hopefully lead to lower search times and fewer deaths and unsolved cases. Causal inference allows causal relationships to be determined, telling SAR teams that they can make current decisions based on these learned relationships and their decisions will cause the change that they expect based on the Bayesian network.