Bayesian theorem is a theorem in probability theory, which describes the probability of an event under some known conditions. ThomasBayes, a British mathematician, first proposed this theorem in a paper published in 1763, which was published by a friend of his after his death.

The Origin of Bayesian Theorem

Thomas Bayes put forward Bayesian theorem to solve the problem of inverse probability, and Bayesian aims to solve the problem of inverse probability in his article. Similarly, taking the lottery as an example, we don't know what is in the lottery bucket, but touch a ball and predict the ratio of white ball to black ball in the bucket by observing the color of the ball.

In fact, this prediction can be made by Bayesian theorem. Bayesian papers at that time only directly tried to solve the inverse probability problem. Bayesian theorem has swept through probability theory and extended its application to various problem fields. The shadow of Bayesian theorem can be seen everywhere where probability prediction is needed. Bayesian method is one of the core methods of machine learning.