In terms of probability, Bayes' Theorem refers to the information that is used to know what the conditional probability of an event is. This concept of Bayes Theorem was developed by the mathematician Thomas Bayes. His intention was to determine the probability of one event relative to the probability of a different event.

## What is the formula of Bayes' Theorem like?

Once we know the definition of Bayes' Theorem, we must take into account what steps we must follow to calculate it and determine the probability that interests us. The formula of Bayes' Theorem is as follows:

In this formula A and B are part of the probable events that must be interpreted. To calculate Bayes' Theorem we will have to take into account the following data. On the one hand, P (A) will be the a priori probability. On the other hand, P (B | A) will be the probability that B has with respect to the given hypothesis of A, which are the likelihoods. Finally, P (A | B) will be the posterior probabilities.

## How is Bayes Theorem applied?

The applications of Bayes' Theorem have been widely criticized over time because it has not always been applied correctly. However, its application is guaranteed as long as both exhaustive and disjoint events can be fulfilled correctly.

Therefore, when faced with the question of what the Bayes Theorem is for and how it can be applied, we know that it is used for a large majority of cases in which it is desired to verify the theory of probability. For example, this Theorem can be used to test what subjective probabilities a certain event can have when we have received some type of previous information. For this, among other reasons, it is considered that Bayes' Theorem can be used to take into account what type of diseases a person can suffer, under an approximate probability based on certain characteristics.

Likewise, in the management of data and information in greater quantities, Bayes' Theorem has its computer application. That is, it can be used to determine the emails considered as *spam*.