3-statistical_Models_in_Simulation

3-statistical_Models_in_Simulation - SYSC4005/5001 Winter...

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Professor John Lambadaris SYSC4005/5001 Winter 2010 1 Statistical Models in Simulation Winter 2010 Slides are based on the texts: -Discrete Event System Simulation, by Banks et al -Discrete Event Simulation: A first Course, by Leemis and Park
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Professor John Lambadaris SYSC4005/5001 Winter 2010 2 Purpose & Overview ± The world the model-builder sees is probabilistic rather than deterministic. ² Some statistical model might well describe the variations. ± An appropriate model can be developed by sampling the phenomenon of interest: ² Select a known distribution through educated guesses ² Make estimate of the parameter(s) ² Test for goodness of fit ± In this module we will: ² Review several important probability distributions ² Present some typical application of these models
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Professor John Lambadaris SYSC4005/5001 Winter 2010 3 Review of Terminology and Concepts ± We will review the following concepts: ² Discrete random variables ² Continuous random variables ² Cumulative distribution function ² Expectation
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Professor John Lambadaris SYSC4005/5001 Winter 2010 4 Discrete Random Variables ± 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 ) ² p(x i ), i = 1,2, … must satisfy: ² 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 . = = 1 1 ) ( 2. all for , 0 ) ( 1. i i i x p i x p
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Professor John Lambadaris SYSC4005/5001 Winter 2010 5 Continuous Random Variables ± 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: ± f(x) , denoted as the pdf of X , satisfies: ± Properties X R X R x x f dx x f R x x f X in not is if , 0 ) ( 3. 1 ) ( 2. in all for , 0 ) ( 1. = = = b a dx x f b X a P ) ( ) ( ) ( ) ( ) ( ) ( . 2 0 ) ( because , 0 ) ( 1. 0 0 0 b X a P b X a P b X a P b X a P dx x f x X P x x < < = < = < = = = =
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Professor John Lambadaris SYSC4005/5001 Winter 2010 6 Continuous Random Variables ± Example: Life of an inspection device is given by X , a continuous random variable with pdf: ² X has an exponential distribution with mean 2 years ² Probability that the device’s life is between 2 and 3 years is: = otherwise , 0 0 x , 2 1 ) ( 2 / x e x f 14 . 0 2 1 ) 3 2 ( 3 2 2 / = = dx e x P x
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Professor John Lambadaris SYSC4005/5001 Winter 2010 7 Cumulative Distribution Function ± Cumulative Distribution Function (cdf) is denoted by F(x) , where F(x) = P(X x) ² If X is discrete, then ² If X is continuous, then ± Properties ± All probability question about X can be answered in terms of the cdf, e.g.: = x x i i x p x F all ) ( ) ( = x dt t f x F ) ( ) ( 0 ) ( lim 3.
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This note was uploaded on 04/16/2010 for the course SCE sysc5001 taught by Professor Lambadaris during the Spring '10 term at Carleton CA.

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3-statistical_Models_in_Simulation - SYSC4005/5001 Winter...

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