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Random Number Generation Fall 2012. By Yaohang Li, Ph.D. Review. Last Class Variance Reduction This Class Random Number Generation Uniform Distribution Non-uniform Distribution Random Number Generation Assignment 3 Next Class Quasi-Monte Carlo. Random Numbers.

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## Random Number Generation Fall 2012

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**Random Number GenerationFall 2012**By Yaohang Li, Ph.D.**Review**• Last Class • Variance Reduction • This Class • Random Number Generation • Uniform Distribution • Non-uniform Distribution Random Number Generation • Assignment 3 • Next Class • Quasi-Monte Carlo**Random Numbers**• Application of Random Numbers • Simulation • Simulate natural phenomena • Sampling • It is often impractical to examine all possible cases, but a random sample will provide insight into what constitutes typical behavior • Numerical analysis • Computer programming • Decision making • “Many executives make their decisions by flipping a coin…” • Recreation**Natural Random Number**• Natural Random Numbers • No two snowflakes are the same • Sources • White Noise • Water Molecule Distribution • etc. • Generation • Measurement • Irreproducible • Errors**Pseudorandom Number Generators**• Pseudorandom Numbers • Using a Mathematical Formula • Deterministic • Behave like real random numbers • Comments • There is no “perfect” pseudorandom number generator • We should never completely trust results from a single pseudorandom number generator • Good random number generators are hard to find**Middle-square method**• Developed by von Neumann • Procedure • 4 digit starting value is created • Square and produce 8-digit number • Get the middle 4 digits as the result and seed for next number • Problems • What if the middle 4 digits are 0 • Forsythe found that the sequence may stuck in 6100, 2100, 4100, 8100, 6100, … • Not a good generator**Quality of Pseudorandom Numbers**• Uniformity • Randomness • Independence • Reproducibility • Portability • Efficiency • A sufficiently long period**Generating Uniform Random Numbers**• Generating Uniform Random Numbers • Uniform distribution on [0,1) • Generation • Un=Xn/m • Xn: Random number Integer • m: Max(Xn)+1: Usually the word size of a computer • Un: Uniform real random number at [0,1)**Linear Congruential Method**• Linear Congruential Method • Most commonly used generator for pseudorandom numbers • m: modulus • a: multiplier • b: additive constant • Period • m constrains the period • max period: 2m-1 • m is usually chosen to be either prime of a power-of-two**Shift-Register Generators (SRG)**• Shift-Register Generators • based on the following recursion • ai and xi are either 0 or 1 • Comments • The recursion produces only bits • Incorporate these bits into integers**Lagged-Fibonacci Generators**• Lagged-Fibonacci Generators (LFG) • Additive Lagged-Fibonacci Generators • Multiplicative Lagged-Fibonacci Generators • Comments • LFG has a much longer period than LCGs • (2k-1)2m-1**Inversive Congruential Generators**• Inversive Congruential Generators (ICGs) • Recursive ICGs • Explicit ICGs • Advantage of ICGs • ICGs do not fall in hyperplanes**Combined Generators**• Combined Generators • Combining different recurrences can increase the period length • Improve the structural properties of pseudorandom generators • Construct a new random sequence • • exclusive-or operator • addition modulo • addition of floating-point random numbers modulo 1 • x, y • Different random number sources**Parallel Random Number Generators**• Requirements of Parallel Random Numbers • Every random number sequence generated on each processor should satisfy the requirements of a good sequential generator. • The parallel generator must be reproducible both on different machines and on the same machine with a different partitioning of the processing resources. • The parallelly generated random streams must be uncorrelated and must not overlap. • The parallel generator should work for an arbitrary, but perhaps bounded, number of processors.**Parallel Random Numbers Generations (Sequence Splitting)**• Sequence Splitting**Parallel Random Numbers Generations (Sequence Splitting)**• Random Tree Method • Also called parameterization method**SPRNG**http://sprng.cs.fsu.edu**Random Choices from a Finite Set**• A random integer X between 0 and k-1 • U is a random number uniformly distributed in [0,1) • A more general case**Inverse Function Method**• Cumulative Distribution Function • Most real-valued distribution may be expressed in terms of its distribution function F(x) • Inverse Function Method • X=F-1(U) • Now the problem reduces to how to evaluate the inverse function F-1()**Interesting Trick**• Generating the random samples of F(x)=x2 • Inverse Function Method • X=U-1/2 • A short cut method • If X1 is a random variable having the distribution F1(x) and if X2 is a random variable having the distribution F2(x) • max(X1, X2) has the distribution F1(x)F2(x) • min(X1, X2) has the distribution F1(x)+F2(x)-F1(x)F2(x) • Then X=max(U1, U2) has the distribution of F(x)=x2 • Hard to believe that max(U1, U2) and U-1/2 have the same distribution**Normal Distribution**• Polar Method • Generate two independent random variables, U1 and U2 • Set V1=2U1-1, V2=2U2-1 • Set S=V1*V1+V2*V2 • If S>=1, return to Step 1 • Set X1 and X2 according to the following two equations**Acceptance-Rejection Method**• Desired pdf • Suppose we bound the desired probability distribution function to sample from a box • Algorithm • Generate a random variable x from U(0,1) • Generate another random variable y from U(0,1) • If x<f(y)/fmax then return y • else repeat from step 1**Acceptance-Rejection Method Example**• Determine an algorithm for generating random variates for a random variable that take values 1, 2, …, 10 with probabilities 0.11, 0.12, 0.09, 0.08, 0.12, 0.10, 0.09, 0.09. 0.10, 0.10 respectively • Acceptance-Rejection Method • u1=U(0,1), u2=U(0,1), c=max(p())=0.12 • Y=floor(10*u1+1) • while (u2>p(Y)/c) • u1=U(0,1), u2=U(0,1) • Y=floor(10*u1+1) • output Y**Analysis of Acceptance-Rejection Method**• Advantage • Acceptance-Rejection Method can fit in different pdfs • popularly used in complicated probability geometry • Disadvantage • Inefficient if the volume of the region of interest is small relative to that of the box • most of the darts will miss the target**Exponential Distribution**• F(x)=1-e-x/ • Logarithm method (inverse function method) • X=-lnU**Shuffling Algorithm**• Let X1, X2, …, Xt be a set of t numbers to be shuffled • j=t • Generate U • Set k=floor (jU)+1 • Exchange Xk with Xj • Decrease j by 1. If j>1, return to step 2**Summary**• Random Numbers • Uniform Random Numbers • Generation of Uniform Random Numbers • Natural Random Number Generators • Pseudorandom Number Generators • Pseudorandom Number Generators • Requirement of Pseudorandom Number Generators • LCG • LFG • SRG • ICG • Combined Random Number Generators • Parallel Random Number Generators • Requirement of Parallel Random Number Generators • Techniques for Parallel Random Number Generators • Leapfrog • Sequence Splitting • Random Tree**Summary**• Numerical Distribution • Random Choices from a finite set • General methods for continuous distributions • inverse function method • acceptance-rejection method • Distributions • Normal distribution • Polar method • Exponential distribution • Shuffling**What I want you to do?**• Review Slides • Review basic probability/statistics concepts

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