PPT-Random Numbers, Number Generators, and Global Health

March 25 2013 Abraham D Flaxman Assistant Professor 2 What is a random number 3 4 5 What is probability 6 7 1 65539 393225 1769499 7077969 8 9 DALYs YLL YLD 10 11 12. gov 301 9754641 Abstract Random Number Generators RNGs are an important building block for algorithms and protocols in cryptography They are paramount in the construction of encryption keys and other cryptographic algorithm parameters In practice sta MATTHEW KAHLE & ELIZABETH MECKE. Presented by Ariel Szapiro. INTRODUCTION : . betti. numbers. Informally, the . k. th. Betti number refers to the number of unconnected . k. -dimensional surfaces. The first few Betti numbers have the following intuitive definitions:. Jake Blanchard. Spring 2010. Uncertainty Analysis for Engineers. 1. A Little History. Initial random number generators were . congruential. For example, a=13, c=0, m=31, x. 0. =1. Uncertainty Analysis for Engineers. THE GENERATION OF PSEUDO-RANDOM NUMBERS . Agenda. generating random number . uniformly. . distributed. Why they are important in simulation. . Why important in General. Numerical . analysis. ,. . random numbers are used in the solution of complicated integrals. . Graham Netherton. Logan Stelly. What is RNG?. RNG = Random Number Generation. Random Number Generators simulate random outputs, such as dice rolls or coin tosses. Traits of random numbers. Random numbers should have a uniform distribution across a range of values. Dr. X. Topics. What Does Randomness Mean?. Randomness in . games. Generating Random Values. Random events in real life: measuring . randomness. What is Randomness?. Unpredictability?. Does it characterize a single event or a sequence of events?. Andy Wang. CIS 5930-03. Computer Systems. Performance Analysis. Generate Random Values. Two steps. Random-number generation. Get a sequence of random numbers distributed uniformly between 0 and 1. Random-. Follow-Up:. Further into Python. Part 3: . Random. Numbers. Part 3: . “Random”. Numbers. Part 3: . Pseudo. -Random. Numbers. Why Random Numbers?. Traditional use: cryptography. http://www.diablotin.com/librairie/networking/puis/figs/puis_0604.gif. Rory Nimmons. Venkat Reddy. U. n. d. e. r. s. t. a. n. d. i. n. g. . R. a. n. d. o. m. n. e. s. s. . Main Concepts. Simulations are used for:. Can help to investigate a query for which there are many potential results, whilst not intending to collect data, and the math is like super difficult to do. . Probabilities Through Simulations. Objective. : . To simulate probabilities using random number tables and random number generators. CHS Statistics. Probabilities Through Simulations. Sometimes we are not sure our theoretical probability is correct. . Genome Informatics . Q. uiz Section . 9. May 26 2015. Random numbers: so intuitive but yet…. Random numbers: so intuitive but yet…. xkcd.com. /221/. People don’t necessarily “get” randomness very well. Linear . congruential. generator (LCG. ). (1). Here . m is called the . modulus, a is a positive integer called the multiplier, and . c . (. which may be zero) is nonnegative integer called the . Based on: William . Stallings, Cryptography and Network Security . . Chapter 7. Pseudorandom Number Generators . and Stream Ciphers. Random Numbers. A number of cryptographic protocols make use of random binary numbers:. Random Numbers. How that process to generate random numbers takes places requires some complicated statistics that are outside the scope of this course. But there are a couple of things to keep in mind:.