3 ms·

Definitely smart thinning, but not sure what makes it Bayesian.

• tanderson92 10y ago
  anti-aircraft fire is random so doesn't specifically target any section of the plane. Therefore, the planes that return to base are a biased sample of the planes that took on damage. See: http://www4.ncsu.edu/~swu6/documents/A_Reprint_Plane_Vulnerability.pdf http://www4.ncsu.edu/~swu6/documents/A_Reprint_Plane_Vulnera...
• smallnamespace 10y ago
  We're interested in the quantity: (1) P(crash | section hit) and adding armour to those sections where that quantity is maximized (maybe with some thought to the relative weight of armour needed for each section, but I digress). Let's directly apply Bayes' rule: (2) P(crash | section hit) = P(section hit | crash) * P(crash) / P(section hit) The denominator can be further expanded: (3) P(section hit) = P(section hit | crash) * P(crash) + P(section hit | no crash) * P(no crash) So we can see from the 2nd term that if aircraft regularly comes back with a section that's been hit and yet it hasn't crashed, then that directly reduces (1), meaning that section needs less relatively less protection, all else being equal. Another point in this method's favor is if crashed aircraft frames are too damaged to permit us to identify which sections were damaged. In that case, we can still estimate (1) just by replacing all the P(section hit | crash) terms with a uniform term. This analysis can be further expanded to the actual amount of damage each section took in a as well. The more damage a section took on surviving aircraft, the less protection it needs.