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Probability theory is a branch of mathematics that studies the law of random phenomena. Its origins in the 17th century century, at that time in the category of error, demographic, life insurance, need to collate and study a large number of random

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I. Bayesian theorem:
Bayesian Theorem explains common knowledge

Chapter II random variables and their distributions
Content Summary:
I. DEFINITION of random variable
Set is a random test, the sample space, if for each sample point, there is a unique fixed number corresponding to it, then the real value function

Chapter three multidimensional random variables and their distributions
Content Summary:
One or two-D random variable
1, the definition of two-dimensional random variable: Set E is a random test, its sample space is defined on the s of the random

3.1 Two-Point Distribution and even distribution1. Two-Point DistributionMany random events have only two results. If the result of the product is qualified or unqualified, the product or reliable work may fail. This type of random event variable

1: For N results in one experiment, if a real-value single-value function is matched for each result, the corresponding value is called a random variable. The mathematical definition is S = {e}. E is a sample in the sample space, and X = x (e) is

1, Probability density function In classifier design (especially Bayesian classifier), when the prior probability of a class and the probability density of a class are both known, determine the discriminant function and decision plane according to

Random Variable
A random variable is different from a common data variable. It is usually expressed in uppercase letters, such as X, Y, and Z. It is not a parameter but a function, that is, a function. For example, the random variable X indicating

In the last chapter, we studied the parameter estimation methods of PDF, which mainly have the maximum likelihood estimation and Bayesian estimation, they mainly estimate the parameters of the PDF with definite form, and in reality, we can't know

1. Concept of Random Variables
As the name implies, a random variable is a variable with a random value. The opposite of a random variable is a "deterministic variable", that is, the variable whose value follows a certain strict rule, such as the

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