1. What is Bayesian Inference
Bayesian inference is a statistical method used to estimate a certain property of a statistic.
It is an application of Bayes' theorem . The English mathematician Thomas Bayes first proposed this theorem in a paper published in 1763. Bayesian inference is distinct from other statistical inference methods. It is based on subjective judgment, that is, you can estimate a value without objective evidence, and then continuously revise it based on actual results. It is precisely because it is too subjective that it has been criticized by many statisticians.
Bayesian inference is computationally intensive and has historically not been widely used for a long time. Only after the birth of the computer did it gain real attention. It has been found that many statistics cannot be judged objectively in advance, and the large data sets in the Internet era, coupled with high-speed computing capabilities, provide convenience for verifying these statistics and create conditions for the application of Bayesian inference. Its power is becoming increasingly apparent.
2. Bayes' Theorem
To understand Bayesian inference, one must first understand Bayes' theorem. The latter is actually a formula for calculating "conditional probability".
The so-called "conditional probability" (Conditional probability) refers to the probability that event A occurs when event B occurs, expressed by P(A|B).
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