Algorithm 1 MCM implementation.

$X[x_1,x_2,x_3,\dots ,x_n];U[u_1,u_2,u_3,\dots ,u_n]$ $M\leftarrow c_1$ //Initialize M (number of iterations) $A[n:M]$ // The array A is declared $A(1,1:M)\leftarrow f(M,x_1,u_1,pdf)$ //Assigns random number with proper PDF $A(2,1:M)\leftarrow f(M,x_2,u_2,pdf)$ $A(3,1:M)\leftarrow f(M,x_3,u_3,pdf)$ ⋮ $A(n,1:M)\leftarrow f(M,x_n,u_n,pdf)$    $Y[n+1:M+2]$ //The array Y is declared $Y(n+1,1:M)\leftarrow g\left(A\right)$ //Function g defines the mathematical model $Y(n+1,M+1)\leftarrow average\left(Y\right(n+1,1:M\left)\right)$ $A(n+1,M+2)\leftarrow standardDeviation\left(Y\right(n+1,1:M\left)\right)$    $B[n:M]\leftarrow h(n,X)$ //The array B is declared with n lines constants    $for$ $i=1$ to n $Z[1:M]\leftarrow B(i:M)$ $B(i:M)\leftarrow A(i:M)$ $Y(i,1:M)\leftarrow g\left(B\right)$ $Y(i,M+1)\leftarrow average\left(Y\right(i,1:M\left)\right)$ $Y(i,M+2)\leftarrow standardDeviation\left(Y\right(i,1:M\left)\right)$ $B(i:M)\leftarrow Z$