Expont-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: Exponent, Sampling, Function, Random, Numbers, State, Histogram, Subplot, Initialize, Generator, Interval

Typology: Exercises

2011/2012

Uploaded on 08/12/2012

ranganath
ranganath 🇮🇳

4.7

(3)

37 documents

1 / 1

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
% Program name; expont.m
% Sampling from Exponential Function
% random numbers from rand function
% in interval [0, 1]
N = 10000;
Max_bins = 50;
rand('state', 0) % initialize the generator to state zero
for k = 1:N
z(k) = 0.; w(k) = 0.;
x = rand;
tx = log(x);
z(k) = -5.0*tx;
w(k) = -1.0*tx;
end
n = hist(z,Max_bins);
m = hist(w,Max_bins);
subplot(2,1,1); bar(n, 'r')
subplot(2,1,2); bar(m, 'b')
% subplot(2,1,1); plot(z)
% subplot(2,1,2); plot(w)
docsity.com

Partial preview of the text

Download Expont-Stochastic Process-Codes and more Exercises Stochastic Processes in PDF only on Docsity!

% Program name; expont.m % Sampling from Exponential Function % random numbers from rand function % in interval [0, 1] N = 10000; Max_bins = 50; rand('state', 0) % initialize the generator to state zero for k = 1:N z(k) = 0.; w(k) = 0.; x = rand; tx = log(x); z(k) = -5.0tx; w(k) = -1.0tx; end n = hist(z,Max_bins); m = hist(w,Max_bins); subplot(2,1,1); bar(n, 'r') subplot(2,1,2); bar(m, 'b') % subplot(2,1,1); plot(z) % subplot(2,1,2); plot(w)

docsity.com