Some Useful Distributions

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# Some Useful Distributions - PowerPoint PPT Presentation

Some Useful Distributions. Binomial Distribution. Bernoulli 1720 k=0:20; y=bino c df(k,20,0.5); s tairs (k,y) grid on. k=0:20; y=binopdf(k,20,0.5); stem(k,y). Binomial Distribution. function y=mybinomial(n,p) for k=0:n

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### Some Useful Distributions

Binomial Distribution

Bernoulli 1720

k=0:20;

y=binocdf(k,20,0.5);

stairs(k,y)

grid on

k=0:20;

y=binopdf(k,20,0.5);

stem(k,y)

Binomial Distribution

function y=mybinomial(n,p)

for k=0:n

y(k+1)=factorial(n)/(factorial(k)*factorial(n-k))*p^k*(1-p)^(n-k)

end

k=0:20;

y=mybinomial(20,0.5);

stem(k,y)

k=0:20;

y=binopdf(k,20,0.1);

stem(k,y)

Geometric Distribution

Warning: Matlab assumes

k=0:20;

y=geopdf(k,0.5);

stem(k,y)

k=0:20;

y=geocdf(k,0.5);

stairs(k,y)

axis([0 20 0 1])

Geometric Distribution

function y=mygeometric(n,p)

for k=1:n

y(k)=(1-p)^(k-1)*p;

end

k=1:20;

y=mygeometric(20,0.5);

stem(k,y)

k=1:20;

y=mygeometric(20,0.1);

stem(k,y)

Poisson Distribution

Poisson 1837

k=0:20;

y=poisspdf(k,5);

stem(k,y)

k=0:20;

y=poisscdf(k,5);

stem(k,y)

grid on

Poisson Distribution

function y=mypoisson(n,lambda)

for k=0:n

y(k+1)=lambda^k/factorial(k)*exp(-lambda);

end

k=0:10;

y=mypoisson(10,0.1);

stem(k,y)

axis([-1 10 0 1])

k=0:10;

y=mypoisson(10,2);

stem(k,y)

axis([-1 10 0 1])

Uniform Distribution

x=0:0.1:8;

y=unifpdf(x,2,6);

plot(x,y)

axis([0 8 0 0.5])

x=0:0.1:8;

y=unifcdf(x,2,6);

plot(x,y)

axis([0 8 0 2])

Normal Distribution

Gauss 1820

x=0:0.1:20;

y=normpdf(x,10,2);

plot(x,y)

Warning: Matlab uses

x=0:0.1:20;

y=normcdf(x,10,2);

plot(x,y)

Normal Distribution

function y=mynormal(x,mu,sigma2)

y=1/sqrt(2*pi*sigma2)*exp(-(x-mu).^2/(2*sigma2));

x=-6:0.1:6;

y1=mynormal(x,0,1);

y2=mynormal(x,0,4);

plot(x,y1,x,y2,'r');

legend('N(0,1)','N(0,4)')

Exponential Distribution

Warning: Matlab assumes

x=0:0.1:5;

y=exppdf(x,1/2);

plot(x,y)

x=0:0.1:5;

y=expcdf(x,1/2);

plot(x,y)

Exponential Distribution

function y=myexp(x,lambda)

y=lambda*exp(-lambda*x);

x=0:0.1:10;

y1=myexp(x,2);

y2=myexp(x,0.5);

plot(x,y1,x,y2,'r')

legend('lampda=2','lambda=0.5')

Rayleigh Distribution

x=0:0.1:10;

y1=raylpdf(x,1);

y2=raylpdf(x,2);

plot(x,y1,x,y2,'r')

legend('sigma=1','sigma=2')

Poisson Approximation to Binomial

n=100;

p=0.1;

lambda=10;

k=0:n;

y1=mybinomial(n,p);

y2=mypoisson(n,lambda);

stem(k,y1)

hold on

stem(k,y2,’r’)

Normal Approximation to Binomial

DeMoivre – Laplace Theorem 1730

If X is a binomial RV is approximately a standard normal RV

A better approximation

Normal Approximation to Binomial

function normbin(n,p)

clf

y1=mybinomial(n,p);

k=0:n;

bar(k,y1,1,'w')

hold on

x=0:0.1:n;

y2=mynormal(x,n*p,n*p*(1-p));

plot(x,y2,'r')

Central Limit Theorem

function k=clt(n) % Central Limit Theorem for sum of dies

m=(1+6)/2; % mean (a+b)/2

s=sqrt(35/12); % standart deviation sqrt(((b-a+1)^2-1)/12)

for i=1:n

x(i,:)=floor(6*rand(1,10000)+1);

end

for i=1:length(x(1,:)) % sum of n dies

y(i)=sum(x(:,i));

z(i)=(sum(x(:,i))-n*m)/(s*sqrt(n));

end

subplot(2,1,1)

hist(y,100)

title('unormalized')

subplot(2,1,2)

hist(z,100)

title('normalized')