Common distribution of knowledge points for machine learning and data mining
Source: Internet
Author: User
Common
distribution of knowledge points for machine learning and data miningCommon Distribution (common distribution):Discrete distribution (discrete type distribution): 0-1 distribution (0-1 distribution)
Definition: If a random variable x x only takes 0 0 and 1 12 values, and its distribution law is
P{X=K}=PK (1−p) 1−k,k=0,1 p\{x=k\}=p^k (1-p) ^{1-k}, k=0,1
where x x obeys the (0−1) (0-1) distribution of the parameter P, which is recorded as X∼ (0−1) X \sim (0-1). Toss a coin once and obey the two-point distribution.
The expectation and variance of the two-point distribution are: P,1−p p, 1-p. Geometric distribution (geometric distribution)
Definition: If the possible value of the random variable x x is,... ,... and its distribution law is
p{x=k}= (1−p) k−1p=qk−1p,k=1,2,3,... p\{x=k\}= (1-p) ^{k-1}p=q^{k-1}p, k=1,2,3,...
The random variable x x obeys the geometric distribution of the parameter P and is recorded as X∼g (p) X \sim G (p).
The geometric distribution has no memory, namely:
p{x>m+n| X>m}=p{x>n} p\{x>m+n| X>m\}=p\{x>n\}
The information of the geometric distribution on the past m failures is forgotten in the subsequent calculations.
The geometric distribution corresponds to: x x is an independent repetition of the Bernoulli test, which is the number of trials of "first success".
The expectation and variance of geometric distributions are: 1p,1−pp
The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion;
products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the
content of the page makes you feel confusing, please write us an email, we will handle the problem
within 5 days after receiving your email.
If you find any instances of plagiarism from the community, please send an email to:
info-contact@alibabacloud.com
and provide relevant evidence. A staff member will contact you within 5 working days.