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Learn how to use the Bayes Theorem within the area of Machine Learning and decision making. As an example we will apply it to drone spotting to differentiate between civilian and military drones spotted in the skies above our house. If you’re unsure about some of the concepts presented in this article the content of this article is also available in video format on the Vinsloev Academy YouTube page: https://youtu.be/pfu0sUdnePc
What is Bayes Theorem?
Bayes Theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if the probability that an incoming spam mail is related to the total presence of the word “Free”, using Bayes Theorem the word “Free” can be used to more accurately assess the probability of a mail being spam than can be done without the knowledge of the words within the mail.
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Drone Spotting
We want to predict the probability of us seeing a military drone in the sky P(M) compared to seeing a civilian drone P(C). Where P stands for probability of either M = Military or C = Civilian. To do so we need to build a model that can be used to calculated the probability of the drone spotting event based on prior knowledge of conditions related to that event.
Lets say we have data which proves that military drones is flying by our house one time a day compared to civilian drones which is flying by times a day. So 4 drone overflights in total on any given day.
One day we see a drone in the sky, but we can’t identify if it was civilian or military. However based on our prior knowledge we know that there is a probability of P(M) = 0.25 and P(C) = 0.75.
