Information theory | Random number generation
Random number generation is a process by which, often by means of a random number generator (RNG), a sequence of numbers or symbols that cannot be reasonably predicted better than by random chance is generated. This means that the particular outcome sequence will contain some patterns detectable in hindsight but unpredictable to foresight. True random number generators can be hardware random-number generators (HRNGS) that generate random numbers, wherein each generation is a function of the current value of a physical environment's attribute that is constantly changing in a manner that is practically impossible to model. This would be in contrast to so-called "random number generations" done by pseudorandom number generators (PRNGs) that generate numbers that only look random but are in fact pre-determined—these generations can be reproduced simply by knowing the state of the PRNG. Various applications of randomness have led to the development of several different methods for generating random data. Some of these have existed since ancient times, among whose ranks are well-known "classic" examples, including the rolling of dice, coin flipping, the shuffling of playing cards, the use of yarrow stalks (for divination) in the I Ching, as well as countless other techniques. Because of the mechanical nature of these techniques, generating large quantities of sufficiently random numbers (important in statistics) required much work and time. Thus, results would sometimes be collected and distributed as random number tables. Several computational methods for pseudorandom number generation exist. All fall short of the goal of true randomness, although they may meet, with varying success, some of the statistical tests for randomness intended to measure how unpredictable their results are (that is, to what degree their patterns are discernible). This generally makes them unusable for applications such as cryptography. However, carefully designed cryptographically secure pseudorandom number generators (CSPRNGS) also exist, with special features specifically designed for use in cryptography. (Wikipedia).
Exploring an amazing pattern that forms when we multiply numbers built only with the one digit
From playlist Number Patterns
From playlist Transformations of the Number Line
This video explains how to determine the prime factorization of a number using a factor tree. http://mathispower4u.yolasite.com/
From playlist Number Sense - Whole Numbers
From playlist Factors, Prime Factors, and Least Common Factors
Javascript lesson 5 - generating random numbers
In this lesson we improve on our quiz by generating 2 random numbers using the Math.random() function to make up the maths question. We then check our answer against the 2 variables to see whether we got it correct. A random maths addition quiz created! Download source file at: http://mag
From playlist Javascript tutorial
Different Types of Numbers on the number line, lesson 1 #shorts
Watch the full playlist: https://www.youtube.com/watch?v=kcxK3_sROZA&list=PL14bv5vXK2WWuODhGbpPQA0GamV5ohOVb&index=1 Natural Numbers (N), (also called positive integers, counting numbers, or natural numbers); They are the numbers {1, 2, 3, 4, 5, …} Whole Numbers (W). This is the set of na
From playlist Celebrities Teach Math: The Number System
Random Number Generation (2 of 2: By spreadsheet)
More resources available at www.misterwootube.com
From playlist Mathematical Exploration
From playlist a. Numbers and Measurement
Procedural Generation: Programming The Universe
In this video I look at how we can manipulate randomness to generate coherent and well formed structures on demand, which allows truly vast and complex resources to be created with no effort from a designer Source: https://github.com/OneLoneCoder/Javidx9/blob/master/PixelGameEngine/Smalle
From playlist Interesting Programming
Cryptography and Network Security by Prof. D. Mukhopadhyay, Department of Computer Science and Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
From playlist Computer - Cryptography and Network Security
Lecture 9 - Random Walk Models
This is Lecture 9 of the COMP510 (Computational Finance) course taught by Professor Steven Skiena [http://www.cs.sunysb.edu/~skiena/] at Hong Kong University of Science and Technology in 2008. The lecture slides are available at: http://www.algorithm.cs.sunysb.edu/computationalfinance/pdf
From playlist COMP510 - Computational Finance - 2007 HKUST
Giray Ökten: Number sequences for simulation - lecture 1
After an overview of some approaches to define random sequences, we will discuss pseudorandom sequences and low-discrepancy sequences. Applications to numerical integration, Koksma-Hlawka inequality, and Niederreiter’s uniform point sets will be discussed. We will then present randomized q
From playlist Probability and Statistics
!!Con 2016 - lol im so random! By Mark Wunsch
lol im so random! By Mark Wunsch Randomness has many applications in computing ranging from cryptography and statistics to generative art and simulation, but where does randomness come from? When you ask for a random number from your system, how truly random is it? This talk will explore
From playlist RailsConf 2016
Stanford Seminar - PCG: A Family of Better Random Number Generators
"PCG: A Family of Better Random Number Generators" - Melissa O'Neill of Harvey Mudd College Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operat
From playlist Engineering
Understanding the basic reproduction number via branching process by Sujit Kumar Nath
Seminar Understanding the basic reproduction number via branching process Speaker: Sujit Kumar Nath (University of Leeds) Date: Wed, 30 September 2020, 15:00 to 16:30 Venue: Online seminar Abstract Branching process is a random process having many applications in physics, biology a
From playlist Seminar Series
PMSP - Computational pseudo-randomness and extractors I - Russell Impagliazzo
Russell Impagliazzo UC San Diego and Institute for Advanced Study June 14, 2010 For more videos, visit http://video.ias.edu
From playlist Mathematics
Lecture 28: Random Numbers - Richard Buckland UNSW (2008)
Extension lecture introducing randomness. What is a random process? How can a deterministic process on a deterministic computer generate random output? Why is randomness useful? What are problems we face when generating random numbers? The lecture introduces Von Neumann's simple algori
From playlist CS1: Higher Computing - Richard Buckland UNSW
How to take random samples using a random digits table
From playlist Unit 4: Sampling and Experimental Design
The Most Powerful Tool Based Entirely On Randomness
We see the effects of randomness all around us on a day to day basis. In this video we’ll be discussing a couple of different techniques that scientists use to understand randomness, as well as how we can harness its power. Basically, we'll study the mathematics of randomness. The branch
From playlist Classical Physics by Parth G