butools.dmap.MarginalDistributionFromDRAP¶

butools.dmap.MarginalDistributionFromDRAP()¶

Matlab:	[alpha, A] = MarginalDistributionFromDRAP(H0, H1, precision)
Mathematica:	{alpha, A} = MarginalDistributionFromDRAP[H0, H1, precision]
Python/Numpy:	alpha, A = MarginalDistributionFromDRAP(H0, H1, precision)

Returns the matrix geometrically distributed marginal distribution of a discrete rational arrival process.

Parameters:	H0 : matrix, shape (M,M) The H0 matrix of the discrete rational arrival process H1 : matrix, shape (M,M) The H1 matrix of the discrete rational arrival process precision : double, optional Numerical precision for checking if the input is valid. The default value is 1e-14
Returns:	alpha : matrix, shape (1,M) The initial vector of the matrix geometrically distributed marginal distribution A : matrix, shape (M,M) The matrix parameter of the matrix geometrically distributed marginal distribution

Examples

For Matlab:

>>> H0 = [0, 0, 0.13; 0, 0.6, 0.18; 0.31, 0.26, 0.02];
>>> H1 = [0, 1, -0.13; 0, 0.18, 0.04; 0.03, 0.09, 0.29];
>>> [a, A] = MarginalDistributionFromDRAP(H0, H1);
>>> disp(a);
     0.021493      0.71253      0.26598
>>> disp(A);
            0            0         0.13
            0          0.6         0.18
         0.31         0.26         0.02

For Mathematica:

>>> H0 = {{0, 0, 0.13},{0, 0.6, 0.18},{0.31, 0.26, 0.02}};
>>> H1 = {{0, 1, -0.13},{0, 0.18, 0.04},{0.03, 0.09, 0.29}};
>>> {a, A} = MarginalDistributionFromDRAP[H0, H1];
>>> Print[a];
{0.021493050580312315, 0.7125271919655068, 0.26597975745418073}
>>> Print[A];
{{0, 0, 0.13},
 {0, 0.6, 0.18},
 {0.31, 0.26, 0.02}}

For Python/Numpy:

>>> H0 = ml.matrix([[0, 0, 0.13],[0, 0.6, 0.18],[0.31, 0.26, 0.02]])
>>> H1 = ml.matrix([[0, 1, -0.13],[0, 0.18, 0.04],[0.03, 0.09, 0.29]])
>>> a, A = MarginalDistributionFromDRAP(H0, H1)
>>> print(a)
[[ 0.02149  0.71253  0.26598]]
>>> print(A)
[[ 0.    0.    0.13]
 [ 0.    0.6   0.18]
 [ 0.31  0.26  0.02]]