PPT-Generating Random Numbers

THE GENERATION OF PSEUDORANDOM 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 . RAN#. Random Sampling using Ran#. The Ran#: Generates . a pseudo . random number to 3 decimal places that . is less than 1.. i.e. . it generates a random number in the range . [0, 1. ]. . Ran#. . is in Yellow. Andrew Ross. Math Dept., Eastern Michigan Univ.. 2009-04-14. Why use random numbers?. Simulating random events: . arrivals to a queueing system. Poker, backgammon, etc.. Doing deterministic computations (like 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. Sources of randomness in a computer?. Methods for generating random numbers:. Time of day (Seconds since midnight). 10438901, 98714982747, 87819374327498,1237477,657418,. Gamma ray . counters. Rand Tables. Sampling . using. RANDOM. Random Sampling using RANDOM. Random: Generates . a pseudo . random number to 3 decimal places that . is less than 1.. i.e. . it generates a random number in the range [0, 1. and Counting Trees. Today’s Plan. Generating functions for basic sequences. Operations on generating functions. Counting. Solve recurrences. Catalan number. Counting Spanning Trees. Generating Functions. Generating Permutations. Many different algorithms have been developed to generate the n! permutations of this set.. We will describe one of these that is based on the . lexicographic . (or . dictionary. A simulation imitates a real situation. Is supposed to give similar results. And so acts as a predictor of what should actually happen. It is a model in which repeated experiments are carried out for the purpose of estimating in real life. 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. 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:. Definition 1: . The . generation function . for the sequence. a. 0. , a. 1. , . . .,. a. k. . ,. . .. of real numbers is the infinite series . G(x) = a. 0. + a. 1. . x + . . .+ . a. k. x. k. +. . . =. 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:. 6. The Excite Poll is an online poll at poll.excite.com. You click on an answer to become part of the sample. One poll question was “Do you prefer watching first-run movies at a movie theater, or waiting until they are available on home video or pay-per-view?” A total of 8896 people responded with 1118 saying they preferred theaters. From this survey you can conclude that . A . simulation technique . uses a probability experiment to mimic a real-life situation.. The . Monte Carlo method . is a simulation technique using random numbers.. Bluman, Chapter 14. 1. Bluman, Chapter 14.