The R programming language has become the standard of fact in statistical analysis. But in this article, I'll show you how easy it is to implement statistical concepts in Python. I'm going to use Python to implement some discrete and continuous probability distributions. Although I will not discuss the mathematical details of these distributions, I will give you some good information on how to learn these statistical concepts in a linked way. Before d
I. Introduction
Recently I have written many learning notes about machine learning, which often involves the knowledge of probability theory. Here I will summarize and review all the knowledge about probability theory for your convenience and share it with many bloggers, I hope that with the help of this blog post, you will be more comfortable reading machine learning documents! Here, I will only make a sum
Title DescriptionXiao Ming is very interested in the probability problem recently. One day, Xiao Ming and Xiao Red play a probability game, first Xiao Ming gives a letter and a word, and then by the small red to calculate the probability that the letter appears in this word. Letters are case insensitive.For example, the given letter is a and the word is Apple, th
In the course of learning Andrew Ng's machine learning, he thought that he had the maximum likelihood estimate, the maximum posterior probability estimate, and the logistic regression, the Bayesian classification of the shutdown was very clear, but after the school pattern recognition course, my outlook on life completely overturned .... Let me give you a word.
First of all, the concept of maximum likelihood (MLE) and maximum posterior
The learning content of probability theory and mathematical statistics originates from Chinese university MOOC, and the fourth edition of the book "Probability Theory and Mathematical Statistics", Zhejiang University.Random PhenomenaUnder certain conditions, it is possible to have a variety of results, and can not know the result before it happens.Randomised TrialsConcept: Observing, recording and experimen
5.1 Probability count. Not too clear test instructions.
5.2 (Find an unordered array) the subject will analyze three algorithms that look for a value x in an unordered array a containing n elements.
Consider the following stochastic strategy: randomly pick a subscript i, if a[i]=x, then terminate, otherwise, continue to pick a a new random subscript, repeat randomly pick subscript, until we find a subscript j, make a[j]=x, or until we have checked eve
Label: How does a style use the SP on BS as application?
Basic knowledge description:
Joint probability:
Definition: refers to the probability that multiple random variables in a probability distribution satisfy their respective conditions at the same time.
For example, if both X and Y are normal distributions, P {x
Conditional
How to Select k elements from n elements with equal probability? This problem is a reservoir sampling. The algorithm can be described as follows:
Init: A reservoir with the size: K
ForI = k + 1ToN
M = random (1, I );
If (M
SwapTheMThValueAndIThValue
End
Someone has provided proof on the Internet and first forwarded it:
[Switch]
Proof:
Each time it is selected based on the probability of K/IFor example, i
Title Description
Transmission Door
The main problem: a pocket of chocolate, chocolate color has c species. Now take out a chocolate from your pocket, if theChocolate is the same color as chocolate on the table, take two chocolates away, or put the chocolate out on the table.The probability of extracting each color of chocolate from the pocket is equally equal. To remove the n chocolate and the remaining M-G on the tableThe
The algorithm for winning the lottery program written in php. Php lottery program winning probability algorithm this article will share with you the php lottery probability algorithm, which can be used for scratch cards, big turntable and other lottery algorithms. The usage is very simple. the code contains detailed comments to the lottery program winning probability
A php probability algorithm instance applicable to lottery programs and random advertisements. Then we will inevitably design algorithms in the program, that is, to give the user a prize based on a certain probability. Let's look at two probability algorithm functions. Algorithm 1: Copy the code as follows: *** the algorithm is designed in the program, that is, t
What is entropy?
Data Compression not only originated from the Information Theory pioneered by clude Shannon in 1940s, but also its basic principle is how small the information can be compressed. So far, it still follows a theorem in information theory, this theorem uses the term "Entropy" (Entropy) in Thermodynamic to indicate the actual amount of information to be encoded in a piece of information:
Consider using a binary number consisting of 0 and 1 to encode a piece of information containing
Sample Space
For a random test, although the results of the test cannot be predicted before each test, all possible result sets of the test are known, the set of all possible results of random test E is called the sample space of E and is recorded as S. The element of the sample space, that is, each possible result of E, is called a sample point. For example, event E: throwing a coin and observing the front h and the opposite t, s = {h, t }.
Frequency Probab
In the R language, different distributions can be generated for experimentation and learning.In R, the probability function is shaped like ①:where the first letter denotes one aspect of the distribution that it refers to:D = density function (density)p = Distribution functions (distribution function)Q = number of decimal functions (quantile function)R = Generate random number (random deviation)Common probability
Import NumPy as NP
import scipy.stats as St
Probability (odds)
P (B) =120: Only one win in 20 gamesOdds against B Winning:o (b) =1−p (b) p (b) =19:A win 19 games, B will win a 1. Create a random variable (rv:random variable)
Poisson distribution:
f_true = 1000
N =
F = St.poisson (f_true). RVs (n)
# Poisson distribution is a discrete probability distribution
You can also do this:
Mu_true, s
Matlab probabilistic density function estimate 2016-03-23 16:12:24
Category: C#/.net
Function: ksdensityFunction: estimate the probability density distribution according to the given dataExample:1. Normal distributionx = Randn (1,100000);[Y,xi] = ksdensity (x);Plot (xi,y, ' Bo ')% verificationOnYn=normpdf (xi,0,1); Probability density function for% standard normal distributionPlot (Xi,yn, ' B ') 2. Rayleig
What is transfer probability matrix?
Transfer probability matrix: each element in the matrix is non-negative, and the sum of elements in each row is equal to 1. Each element is represented by probability. Under certain conditions, it is transferred to each other. Therefore, it is called the transfer probability matrix.
Website activities sometimes involve activities and activities to attract users to register and improve the user activity of the website. At the same time, participating users will receive certain prizes, including 100% of the prize winners and a certain probability.
Website activities sometimes involve activities and activities to attract users to register and improve the user activity of the website. At the same time, participating users will receiv
I. Classical and geometrical approximate 1.1 classical and geometrical approximate features 1) in common: equal probabilities (the probability of each occurrence being the same) 2) difference:
The classical approximate sample space is a finite set.
A geometric profile can be an infinite set, but it can be represented by a geometric region
1.2 equation 1) Classical Overview: The number of basic events known as N and the result of the
minimum values in the sequence. The employment problem is to build a model for the updated frequency of the current winning member.
Worst case
In the worst case, we hire every candidate, M = n.
Probability Analysis
In fact, we neither know the order in which candidates appear nor control this order, so we use probability analysis. Probability Analysis is to use
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.