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IEOR 161 HW 7 PRINTOUTS

# IEOR 161 HW 7 PRINTOUTS - t=1:200 N(t)=ctr i=rand(1 if...

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IEOR 161 HW 7 PRINTOUTS, Eddie Lo

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PART A Each of these histograms represents the number of times the population value hits the number at each step. It shows how the number in the population tomorrow depends on the population today, going up with births and down by deaths. Even though it’s more likely for a death, there is still a normal distribution at each step. PART B This shows only half of the histograms I’ve just constructed in part A, suggesting the geometric random variable will give half of the Markov Chain.
MATLAB CODE clear all for k=1:10000 ctr=0; for
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Unformatted text preview: t=1:200 N(t)=ctr; i=rand(1); if i<=.35 ctr=ctr+1; else if i>.35 & i<=.45 ctr=ctr; else ctr=ctr-1; end N(t)=ctr; end end A(k) = N(5); B(k) = N(10); C(k) = N(20); D(k) = N(50); E(k) = N(75); F(k) = N(100); G(k) = N(200); end hist(A,100) title( 'N(5)' ) figure hist(B,100) title( 'N(10)' ) figure hist(C,100) title( 'N(20)' ) figure hist(D,100) title( 'N(50)' ) figure hist(E,100) title( 'N(75)' ) figure hist(F,100) title( 'N(100)' ) figure hist(G,100) title( 'N(200)' ) for i=1:10000 R(i) = geornd(1-(.35/.55)); end hist(R,50) title('Geometric RV’)...
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• Spring '08
• Lim,A
• Probability distribution, Probability theory, probability density function, Markov chain, Eddie Lo, figure hist

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