Naive Bayes is the model with Trust Issues

How the Naive Bayes Probabilistic Model works BTS.

I flew to Georgia a month ago from Hartford; the weather here is pleasant considering there’s knee-deep snow back home. Anyway, I was bummed I couldn’t carry my vibrator with me because of the battery restrictions during the airport security check. Considering this vivid scenario; X-Ray scanners flag some bags as containing dangerous items (weapons, explosives). The only thing dangerous about my vibrator is it’s ability to give immense pleasure. So the question is, does every flagged bag actually contain something dangerous? No, of course not; many are false alarms like my beautiful vibrator.

Prerequisites

A is the event that a bag actually contains a dangerous item

B is the event that a bag is flagged by a scanner as dangerous

1. Posterior Probability P(A|B): Probability that a flagged bag actually contains a dangerous item.
2. Likelihood P(B|A): The probability that an actual dangerous bag gets flagged.
3. Prior Probability P(A): Key being “prior” probability that a bag constains a dangerous item before scanning.
4. Marginal Probability P(B): Probability of a bag being flagged (real or false alarm)