butools.dmap.MarginalMomentsFromDMMAP¶

butools.dmap.MarginalMomentsFromDMMAP()¶

Matlab:	moms = MarginalMomentsFromDMMAP(D, K, precision)
Mathematica:	moms = MarginalMomentsFromDMMAP[D, K, precision]
Python/Numpy:	moms = MarginalMomentsFromDMMAP(D, K, precision)

Returns the moments of the marginal distribution of a discrete marked Markovian arrival process.

Parameters:	D : list/cell of matrices of shape(M,M), length(N) The D0...DN matrices of the DMMAP K : int, optional Number of moments to compute. If K=0, 2*M-1 moments are computed. The default value is K=0. precision : double, optional Numerical precision for checking if the input is valid. The default value is 1e-14
Returns:	moms : row vector of doubles, length K The vector of moments.

Examples

For Matlab:

>>> D0 = [0.34, 0, 0; 0.06, 0.05, 0.03; 0.11, 0.13, 0];
>>> D1 = [0.3, 0, 0; 0.16, 0.18, 0.05; 0.15, 0.04, 0.09];
>>> D2 = [0, 0.01, 0; 0.1, 0.07, 0.08; 0.13, 0.12, 0.13];
>>> D3 = [0.35, 0, 0; 0, 0.18, 0.04; 0.06, 0.03, 0.01];
>>> moms = MarginalMomentsFromDMMAP({D0, D1, D2, D3});
>>> disp(moms);
       1.5037       3.0278       8.4243       31.097       143.88

For Mathematica:

>>> D0 = {{0.34, 0, 0},{0.06, 0.05, 0.03},{0.11, 0.13, 0}};
>>> D1 = {{0.3, 0, 0},{0.16, 0.18, 0.05},{0.15, 0.04, 0.09}};
>>> D2 = {{0, 0.01, 0},{0.1, 0.07, 0.08},{0.13, 0.12, 0.13}};
>>> D3 = {{0.35, 0, 0},{0, 0.18, 0.04},{0.06, 0.03, 0.01}};
>>> moms = MarginalMomentsFromDMMAP[{D0, D1, D2, D3}];
>>> Print[moms];
{1.503697331491185, 3.027841857350894, 8.424305390832199, 31.097386717744087, 143.8804184010114}

For Python/Numpy:

>>> D0 = ml.matrix([[0.34, 0, 0],[0.06, 0.05, 0.03],[0.11, 0.13, 0]])
>>> D1 = ml.matrix([[0.3, 0, 0],[0.16, 0.18, 0.05],[0.15, 0.04, 0.09]])
>>> D2 = ml.matrix([[0, 0.01, 0],[0.1, 0.07, 0.08],[0.13, 0.12, 0.13]])
>>> D3 = ml.matrix([[0.35, 0, 0],[0, 0.18, 0.04],[0.06, 0.03, 0.01]])
>>> moms = MarginalMomentsFromDMMAP([D0, D1, D2, D3])
>>> print(moms)
[1.503697331491185, 3.0278418573508938, 8.424305390832199, 31.097386717744087, 143.88041840101141]