Monte Normal Dist M-Stochastic Process-Codes, Exercises of Stochastic Processes

Main topics for this course are Stochastic process, random variables, linear congruent generators, pdfs and cdfs, rejection method, metropolis methods, sampling techniques, random walks and genetic algorithm. This matlab code includes: Monte, Normal, Distribution, Random, Function, Interval, Delta, Initialize, Generator, State, Histogram, Peobability, Density

Typology: Exercises

2011/2012

Uploaded on 08/12/2012

ranganath
ranganath 🇮🇳

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% Program name; monte_normal_dist.m
% random numbers from rand function in interval [0, 1]
N = 10;
NN = 2000;
xlow_limit = -5.0;
delta = 0.1;
Max_bins = 21;
for i=1:NN
xx(i) =0.0;
end
rand('state', 0) % initialize the generator to state zero
axis([0 1 0 1])
hold on
for k = 1: NN
x1 = 0; x2 = 0;
x1 = rand;
x2 = rand;
k
plot(x1, x2, 'r:.')
hold on
m(k)= getframe;
end
% movie(m, 30) % play movie 30 times.
% plot a histogram to show probability density
% hist(xx, 1000)
%
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% Program name; monte_normal_dist.m % random numbers from rand function in interval [0, 1] N = 10; NN = 2000; xlow_limit = -5.0; delta = 0.1; Max_bins = 21; for i=1:NN xx(i) =0.0; end rand('state', 0) % initialize the generator to state zero axis([0 1 0 1]) hold on for k = 1: NN x1 = 0; x2 = 0; x1 = rand; x2 = rand; k plot(x1, x2, 'r:.') hold on m(k)= getframe; end % movie(m, 30) % play movie 30 times. % plot a histogram to show probability density % hist(xx, 1000) %

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