Ever wondered about calculating the probability of an event based on some other event, it would have been fun right! If till now you have not figured out trying the above scenario, I will help you and explain how it is possible and also it is too much fun.
In mathematics world, if we are trying to find out the probability of an event based on occurrence of another event, we denote it with_ “|”__ symbol between the two events. For Example, P(Happy | Watched a movie), where “P” represent Probability and the inner two are events, and the above equation signifies the probability of a person being happy given that he/she has watched a movie[I have taken this example in context with a person]._
Bayes Theorem Equation
In the given image above, “A” & “B” are events, and in order to find the probability of occurrence of event “A” given that event “B” has already occurred, is calculated by the above equation, where “P” always represents “probability.”
Bayes Theorem is based on the 4 concepts, those are “Likelihood”, “Prior Probability”, “Marginal Probability”, & “Posterior Probability”!
Prior Probability
It signifies the probability of the occurrence of an event(our hypothesis) before the occurrence of another event or we can say before observing the evidence.
Marginal Probability
It signifies the probability of the occurrence of an event(actual event/evidence or the event which we are considering to already occur) to occur in all the possibilities of our hypothesis.
Likelihood
It signifies the probability of an event to be true/ or to occur if we consider our hypothesis to be true, or we can say that another event(out hypothesis) has already occurred.
Posterior Probability
It signifies the probability of occurrence of an event (our hypothesis) given that another event has already occurred.
If we are given with the first 3 parameters from the above list of 4 parameters, we can easily predict the final one using the Bayes Theorem.
#deep-learning #bayes-theorem #machine-learning #naive-bayes #data-science #data analysis
