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How to implement these five kinds of powerful probability distributions in Python

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

Summary of probability theory knowledge in Machine Learning

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

The letter probability----The C language face question in the smart mail

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

Maximum likelihood estimation vs max posteriori probability estimation, logistic regression vs Bayes classification

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

Chapter One basic concepts of probability theory

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

The fifth chapter of algorithm introduction probability analysis and stochastic algorithm last study questions

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

[Probability theory] Bayesian Law

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

[Application of reservoir sampling] How to Select k elements from n elements with equal probability

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

POJ 1322 chocolate (generating function | | Probability DP)

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

Php lottery program winning probability algorithm-PHP Tutorial

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

PHP probability algorithm instance for lottery programs and random advertisements _ PHP Tutorial

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

Stupid Data Compression tutorial-Chapter 2 technical preparation: probability, model, and encoding

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

Probability of mathematical foundation

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

R language Learning Note-probability function

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

scipy.stats--probability, random variable and distribution _numpy

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

Estimation of probability density function of matlab

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

Transfer probability matrix)

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.

Example of the PHP probability algorithm applicable to lottery programs and random advertisements

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

Probability statistics of "mathematical Statistics"

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

Introduction to algorithms Chapter 5: Probability Analysis and Random Algorithms

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

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