butools.ph.SamplesFromPH¶

butools.ph.SamplesFromPH()¶

Matlab:	x = SamplesFromPH(alpha, A, K, prec)
Mathematica:	x = SamplesFromPH[alpha, A, K, prec]
Python/Numpy:	x = SamplesFromPH(alpha, A, K, prec)

Generates random samples from a phase-type distribution.

Parameters:	alpha : matrix, shape (1,M) The initial probability vector of the phase-type distribution. A : matrix, shape (M,M) The transient generator matrix of the phase-type distribution. K : integer The number of samples to generate. prec : double, optional Numerical precision to check if the input phase-type distribution is valid. The default value is 1e-14.
Returns:	x : vector, length(K) The vector of random samples

Examples

For Matlab:

>>> a = [0.1,0.9,0];
>>> A = [-6.2, 2, 0; 2, -9, 1; 1, 0, -3];
>>> x = SamplesFromPH(a, A, 1000);
>>> mt = MarginalMomentsFromTrace(x, 3);
>>> disp(mt);
      0.19635     0.091647     0.070132
>>> mp = MomentsFromPH(a, A, 3);
>>> disp(mp);
      0.20939      0.10449     0.089092

For Mathematica:

>>> a = {0.1,0.9,0};
>>> A = {{-6.2, 2, 0},{2, -9, 1},{1, 0, -3}};
>>> x = SamplesFromPH[a, A, 1000];
>>> mt = MarginalMomentsFromTrace[x, 3];
>>> Print[mt];
{0.20740615925124653, 0.10656742833921845, 0.10221807499522584}
>>> mp = MomentsFromPH[a, A, 3];
>>> Print[mp];
{0.20938722294654497, 0.10448912014333092, 0.08909150039190732}

For Python/Numpy:

>>> a = ml.matrix([[0.1,0.9,0]])
>>> A = ml.matrix([[-6.2, 2, 0],[2, -9, 1],[1, 0, -3]])
>>> x = SamplesFromPH(a, A, 1000)
>>> mt = MarginalMomentsFromTrace(x, 3)
>>> print(mt)
[0.21509604778795494, 0.11170343335004232, 0.099402940114649896]
>>> mp = MomentsFromPH(a, A, 3)
>>> print(mp)
[0.20938722294654497, 0.10448912014333091, 0.089091500391907288]