uniform-distribution | 易学教程

Plot normalized uniform mixture

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问题 I need to reproduce the normalized density p(x) below, but the code given does not generate a normalized PDF. clc, clear % Create three distribution objects with different parameters pd1 = makedist('Uniform','lower',2,'upper',6); pd2 = makedist('Uniform','lower',2,'upper',4); pd3 = makedist('Uniform','lower',5,'upper',6); % Compute the pdfs x = -1:.01:9; pdf1 = pdf(pd1,x); pdf2 = pdf(pd2,x); pdf3 = pdf(pd3,x); % Sum of uniforms pdf = (pdf1 + pdf2 + pdf3); % Plot the pdfs figure; stairs(x,pdf,

Sample uniformly in a multidimensional ring without rejection

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问题 The algorithm in this question tells us how to efficiently sample from a multidimensional ball. Is there a way to similarly efficiently sample from a multidimensional ring , i.e. have r1<r<r2 I hope that a not too complex modification of that scaling function r*(gammainc(s2/2,n/2).^(1/n))./sqrt(s2) is possible. (Mediocrity disclaimer: haven't even figured the algebra/geometry for the original scaling function yet). Original matlab code copypasted: function X = randsphere(m,n,r) % This

How to generate a number representing the sum of a discrete uniform distribution

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问题 Step 1: Let's say that I want to generate discrete uniform random numbers taking the value -1 or 1. So in other words I want to generate numbers having the following distribution: P(X = -1) = 0.5 P(X = 1) = 0.5 To generate an array of 100 of those numbers I can write this code: n = 100 DV = [-1,1]; % Discrete value RI = unidrnd(2,n,1); % Random uniform index DUD = DV(RI); % Discrete uniform distribution My DUD array looks like: [-1,1,1,1,-1,-1,1,-1,...] Step 2: Now I would like to generate 10

Random numbers that add to 100: Matlab

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问题 [I'm splitting a population number into different matrices and want to test my code using random numbers for now.] Quick question guys and thanks for your help in advance - If I use; 100*rand(9,1) What is the best way to make these 9 numbers add to 100? I'd like 9 random numbers between 0 and 100 that add up to 100. Is there an inbuilt command that does this because I can't seem to find it. 回答1: I see the mistake so often, the suggestion that to generate random numbers with a given sum, one

Distribution of Random Numbers

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问题 I have two options of code: Option 1 int myFunc() { return new Random().nextInt(); } Or: Option 2 private static final Random random = new Random(); int myFunc() { return random.nextInt(); } I understand that option 2 is more idiomatic. I am wondering about the validity of option 1 . In option 1 I will only ever use the first number generated by a given seed. In option 2 I choose a seed and generate n numbers using that seed. IIUC the guarantees on randomness are on this use case. My question

ECDF plot from a truncated MD5

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问题 In this link, it says that truncated MD5 is uniformly distributed. I wanted to check it using PySpark and I created 1,000,000 UUIDs in Python first as shown below. Then truncated the first three characters from MD5. But the plot I get is not similar to the cumulative distribution function of a uniform distribution. I tried with UUID1 and UUID4 and the results are similar. What is the right way of conforming the uniform distribution of truncated MD5? import uuid import numpy as np import

Will “min to max” uniform real distribution produce Inf,-Inf, or NaN?

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问题 If I were to produce floating point values in the following way: template <typename T> T RandomFromRange(T low, T high){ std::random_device random_device; std::mt19937 engine{random_device()}; std::uniform_real_distribution<T> dist(low, high); return dist(engine); } template <typename T> T GetRandom(){ return RandomFromRange (std::numeric_limits<T>::min(),std::numeric_limits<T>::max()); } //produce floating point values: auto num1 = GetRandom<float>(); auto num2 = GetRandom<float>(); auto

Will “min to max” uniform real distribution produce Inf,-Inf, or NaN?

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If I were to produce floating point values in the following way: template <typename T> T RandomFromRange(T low, T high){ std::random_device random_device; std::mt19937 engine{random_device()}; std::uniform_real_distribution<T> dist(low, high); return dist(engine); } template <typename T> T GetRandom(){ return RandomFromRange (std::numeric_limits<T>::min(),std::numeric_limits<T>::max()); } //produce floating point values: auto num1 = GetRandom<float>(); auto num2 = GetRandom<float>(); auto num3 = GetRandom<float>(); //... Is it possible that I will ever get back a NaN , Inf , or -Inf ? Let's

Distribution of Random Numbers

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I have two options of code: Option 1 int myFunc() { return new Random().nextInt(); } Or: Option 2 private static final Random random = new Random(); int myFunc() { return random.nextInt(); } I understand that option 2 is more idiomatic. I am wondering about the validity of option 1 . In option 1 I will only ever use the first number generated by a given seed. In option 2 I choose a seed and generate n numbers using that seed. IIUC the guarantees on randomness are on this use case. My question is, therefore, if I call option 1 many times are there any guarantees on the uniformity of the

How to generate a number representing the sum of a discrete uniform distribution

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Step 1: Let's say that I want to generate discrete uniform random numbers taking the value -1 or 1. So in other words I want to generate numbers having the following distribution: P(X = -1) = 0.5 P(X = 1) = 0.5 To generate an array of 100 of those numbers I can write this code: n = 100 DV = [-1,1]; % Discrete value RI = unidrnd(2,n,1); % Random uniform index DUD = DV(RI); % Discrete uniform distribution My DUD array looks like: [-1,1,1,1,-1,-1,1,-1,...] Step 2: Now I would like to generate 10 numbers equal to sum(DUD) , so 10 numbers having a distribution corresponding to the sum of 100