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This article introduces the relevant knowledge of "what is Bayesian Bayes Theorem". In the operation of actual cases, many people will encounter such a dilemma. Then let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

After reading the Bayesian theorem, most articles explain the Bayesian formula step by step, explaining this great formula with abstract examples, such as calculating the incidence rate, calculating the drug use rate and even calculating the probability of the slag girl. for this reason, we have come up with a bunch of abstract words such as "prior rate" and "posterior rate" to explain the connotation of the formula. A proposition is not clear and come up with some new words and concepts, but pollute the purity and simplicity of the formula itself.

We try to imagine how Bayes came up with this theorem. It is all why human beings can figure out how his logic of thinking is formed. This question makes it clear that it is of great significance for human beings to engage in work. Instead of memorizing other people's formulas, deceiving people with concepts and high-sounding words as "terms".

Suppose there are A sets, B sets, An and B intersect, and A set B is a complete set. This is the known condition that Bayes knows. Bayes likes to ponder, what is he thinking about? That is, both An and B are probabilities. How do you describe the probability of A ∩ B? because when some people think about eight-part essays and spend their whole lives fighting for fame, Bayesian's pursuit is how to express the natural world with a formula, the simpler the better, as for where it can be used later is not his concern.

Because An and B intersect, how much does B account for in A? How much does An account for in B? Regardless of whether it is probabilistic or not, Thomas Bayes gives the first abstract representation, namely

A ∩ Bhand B, the proportion of intersection in B, whereas A ∩ Bhand An is the proportion of intersection in A.

A | ratio A ∩ Bash B (1) intersection in B

B | proportion of intersection A (2) of A ∩ Bash A

There are common terms in formulas (1) and (2), and Thomas Bayes does not hesitate to cancel them out to simplify the formula, that is,

A | B / (B | A) = A / B

As mentioned above, A _ B refers to the probability of different events, that is

P (A | B) = P (A) * P (B | A) / P (B) (3)

Formula (3) is the famous Bayesian theorem formula, which is discovered after his death and makes a great contribution to the conditional probability.

That is to say

P (A) and P (B) represent the probability of two different events respectively. Bei Ye wants to know the probability of A happening when B event occurs. This probability is equivalent to the combination of P (A) and a factor, which is P (B | A) / P (B), that is to say, the ratio of the probability of B occurrence to P (B) when An event occurs.

Further,

If you want to calculate the probability of occurrence of event B, it can be understood that the probability of An itself is disturbed by a factor, which may amplify the probability of An itself or reduce the probability of An itself. if An occurs, the greater the probability of occurrence of B, the greater the probability of P (A | B), the greater the P (B), the smaller P (A | B), which is inversely proportional.

Therefore, the more times girls go to nightclubs, the more likely they are to be scum girls. P (dregs) = P (scum girls) * P (nightclubs) / P (nightclubs), the key factor P (nightclubs). The fact that scum girls are often present in nightclubs increases the probability that girls in nightclubs are scum girls.

Then we can summarize Bei Yeh's theorem in a short sentence:

P (A | B) is directly proportional to P (B | A) and inversely proportional to P (B).

This is the end of what is Bayesian Bayes Theorem. Thank you for your reading. If you want to know more about the industry, you can follow the website, the editor will output more high-quality practical articles for you!

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