Chapter 5 Statistical Models in Simulation Banks, Carson, Nelson - PowerPoint PPT Presentation

Chapter 5 Statistical Models in Simulation Banks, Carson, Nelson & Nicol Discrete-Event System Simulation

Outlines  Purpose & Overview  Discrete random variables  Continuous random variables  Cumulative distribution function  Expectation  Empirical distribution  Discrete distributions  Continuous distributions  Useful Statistical Models  Poisson Process 2

Purpose & Overview  The world the model-builder sees is probabilistic rather than deterministic.  Some statistical model might well describe the variations.  An appropriate model can be developed by sampling the phenomenon of interest:  Select a known distribution through educated guesses  Make estimate of the parameter(s)  Test for goodness of fit  In this chapter:  Review several important probability distributions  Present some typical application of these models 3

Discrete Random Variables [Probability Review]  X is a discrete random variable if the number of possible values of X is finite, or countably infinite.  Example: Consider jobs arriving at a job shop.  Let X be the number of jobs arriving each week at a job shop. R x = possible values of X (range space of X ) = {0,1,2 ,…}  p(x i ) = probability the random variable is x i = P(X = x i )   p(x i ), i = 1,2 , … must satisfy:  1. ( ) 0 , for all p x i i    2. p ( x ) 1  i i 1  The collection of pairs [x i , p(x i )], i = 1,2 ,…, is called the probability distribution of X , and p(x i ) is called the probability mass function (pmf) of X . 4

Discrete Random Variables [Probability Review]  Example: Assume the die is loaded so that the probability that a given face lands up is proportional to the number of spots showing. x i 1 2 3 4 5 6 P(x i ) 1/21 2/21 3/21 4/21 5/21 6/21  p(x i ), i = 1,2 , … must satisfy:  1. p ( x ) 0 , for all i i    2. p ( x ) 1  i i 1 5

Continuous Random Variables [Probability Review]  X is a continuous random variable if its range space R x is an interval or a collection of intervals.  The probability that X lies in the interval [a,b] is given by:  b    P ( a X b ) f ( x ) dx a  f(x) , denoted as the pdf of X , satisfies:  1. f ( x ) 0 , for all x in R X   2. ( ) 1 f x dx R X  3. f ( x ) 0 , if x is not in R X  Properties  x    0 1. ( P X x ) 0, because f x dx ( ) 0 0 x 0            2. ( P a X b ) P a ( X b ) P a ( X b ) P a ( X b ) 6

Continuous Random Variables [Probability Review]  Example: Life of an inspection device is given by X , a continuous random variable with pdf:  1    x / 2 e , x 0   f ( x ) 2   0 , otherwise  X has an exponential distribution with mean 2 years  Probability that the device’s life is between 2 and 3 years is: 1  3      x / 2 P ( 2 x 3 ) e dx 0 . 14 2 2 7

Cumulative Distribution Function [Probability Review]  Cumulative Distribution Function (cdf) is denoted by F(x) , where F(x) = P(X <= x)    If X is discrete, then F ( x ) p ( x ) i all  x x i  If X is continuous, then   x  F ( x ) f ( t ) dt   Properties   1. is nondecreasing function. If , then ( ) ( ) F a b F a F b  2. lim F x ( ) 1  x  3. lim F x ( ) 0  x  All probability question about X can be answered in terms of the cdf, e.g.:      P a ( X b ) F b ( ) F a ( ), for all a b 8

Cumulative Distribution Function [Probability Review]  Example: The die-tossing experiment described in last example has a cdf given as follows: (- ∞,1) [6, ∞) x [1,2) [2,3) [3,4) [4,5) [5,6) F(x) 0 1/21 3/21 6/21 10/21 15/21 21/21  [a,b) = {a ≤ x < b} 9

Cumulative Distribution Function [Probability Review]  Example: An inspection device has cdf:   1 x     t / 2 x / 2 F ( x ) e dt 1 e 2 0  The probability that the device lasts for less than 2 years: 1          P ( 0 X 2 ) F ( 2 ) F ( 0 ) F ( 2 ) 1 e 0 . 632  The probability that it lasts between 2 and 3 years:            ( 3 / 2 ) 1 P ( 2 X 3 ) F ( 3 ) F ( 2 ) ( 1 e ) ( 1 e ) 0 . 145 10

Expectation [ Probability Review]  The expected value of X is denoted by E(X)   If X is discrete  E ( x ) x p ( x ) i i all i    If X is continuous  E ( x ) xf ( x ) dx    The mean, m, or the 1 st moment of X  A measure of the central tendency  The variance of X is denoted by V(X) or var(X) or s 2 V(X) = E[(X – E[X] 2 ]  Definition: V(X) = E(X 2 ) – [E(x)] 2  Also,  A measure of the spread or variation of the possible values of X around the mean  The standard deviation of X is denoted by s  Definition: square root of V(X)  Expressed in the same units as the mean 11

Expectations [ Probability Review ]  Example: The mean of life of the previous inspection device is:    1   x / 2         xe x / 2 x / 2 E ( X ) xe dx e dx 2 2 0 0 0  To compute variance of X , we first compute E(X 2 ) :      1 / 2 x       2   x e 2 2 x / 2 x / 2 E ( X ) x e dx e dx 8 2 0 0 0  Hence, the variance and standard deviation of the device’s life are:    2 ( ) 8 2 4 V X s   V ( X ) 2 12

Empirical Distributions [Probability Review]  Example:  Customers arrive at lunchtime in groups of from one to eight persons.  The number of persons per party in the last 300 groups has been observed.  The results are summarized in a table.  The histogram of the data is also included.

Empirical Distributions (cont.) [Probability Review] Arrivals Frequenc Relative Cumulati per Party y Frequenc ve y Relative Frequenc y 1 30 0.10 0.10 2 110 0.37 0.47 3 45 0.15 0.62 4 71 0.24 0.86 5 12 0.04 0.90 6 13 0.04 0.94 7 7 0.02 0.96 8 12 0.04 1.00

Empirical Distributions (cont.) [Probability Review]  The CDF in the figure is called the empirical distribution of the given data.

Empirical Distributions [Probability Review]  Example:  The time required to repair a system that has suffered a failure has been collected for the last 100 instances.  The empirical CDF is shown in the figure

Empirical Distributions (cont.) [Probability Review] Intervals Frequency Relative Cumulativ (Hours) Frequency e Frequency 0<x<0.5 21 0.21 0.21 0.5<x<1.0 12 0.12 0.33 1.0<x<1.5 29 0.29 0.62 1.5<x<2.0 19 0.19 0.81 2.0<x<2.5 8 0.08 0.89 2.5<x<3.0 11 0.11 1.00

Discrete Distributions  Discrete random variables are used to describe random phenomena in which only integer values can occur.  In this section, we will learn about:  Bernoulli trials and Bernoulli distribution  Binomial distribution  Geometric and negative binomial distribution  Poisson distribution 18

Bernoulli Trials and Bernoulli Distribution [Discrete Dist’n]  Bernoulli Trials:  Consider an experiment consisting of n trials, each can be a success or a failure.  Let X j = 1 if the jth experiment is a success  and X j = 0 if the jth experiment is a failure  The Bernoulli distribution (one trial):    p , x 1 , j 1 , 2 ,..., n j         p ( x ) p ( x ) 1 p q , x 0 ,j 1 , 2 ,...,n j j j j   0 , otherwise  where E(X j ) = p and V(X j ) = p(1-p) = pq  Bernoulli process:  The n Bernoulli trials where trails are independent: p(x 1 ,x 2 ,…, x n ) = p 1 (x 1 )p 2 (x 2 ) … p n (x n ) 19

Binomial Distribution [Discrete Dist’n]  The number of successes in n Bernoulli trials, X , has a binomial distribution.    n      x n x p q , x 0 , 1 , 2 ,..., n     p ( x )   x   0 , otherwise The number of Probability that outcomes having the there are required number of x successes and successes and (n-x) failures failures  The mean, E(x) = p + p + … + p = n*p  The variance, V(X) = pq + pq + … + pq = n*pq 20

Binomial Distribution [Discrete Dist’n]  Example A production process manufactures computer chips on the average at 2% nonconforming. Every day, a random sample of size 50 is taken from the process. If the sample contains more than two nonconforming chips, the process will be stopped. Compute the probability that the process is stopped by the sampling scheme. 21

Binomial Distribution [Discrete Dist’n]   Solution 50 (0.02) (0.98)   x 50 x   , x 0,1,2,...,50    p x ( ) x   0, otherwise     ( 2) 1 ( 2) P X P X   50 2     x 50 x   ( 2) (0.02) (0.98) P X   x  x 0    50 49 2 48 (0.98) 50(0.02)(0.98) 1225(0.02) (0.98)  0.92 P X     ( 2) 1 0.92 0.08 22