In this case, a Monte Carlo sample corresponds to one time unit \( \Delta t \).
The following simple C/C++-program illustrates this model.
// Particles in a box
#include <iostream>
#include <fstream>
#include <iomanip>
#include "lib.h"
using namespace std;
ofstream ofile;
int main(int argc, char* argv[])
{
char *outfilename;
int initial_n_particles, max_time, time, random_n, nleft;
long idum;
// Read in output file, abort if there are too few command-line arguments
if( argc <= 1 ){
cout << "Bad Usage: " << argv[0] <<
" read also output file on same line" << endl;
exit(1);
}
else{
outfilename=argv[1];
}
ofile.open(outfilename);
// Read in data
cout << "Initial number of particles = " << endl ;
cin >> initial_n_particles;
// setup of initial conditions
nleft = initial_n_particles;
max_time = 10*initial_n_particles;
idum = -1;
// sampling over number of particles
for( time=0; time <= max_time; time++){
random_n = ((int) initial_n_particles*ran0(&idum));
if ( random_n <= nleft){
nleft -= 1;
}
else{
nleft += 1;
}
ofile << setiosflags(ios::showpoint | ios::uppercase);
ofile << setw(15) << time;
ofile << setw(15) << nleft << endl;
}
return 0;
} // end main function