butools.dph.SamplesFromDPH¶

butools.dph.SamplesFromDPH()¶

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

Generates random samples from a discrete phase-type distribution.

Parameters:	alpha : matrix, shape (1,M) The initial probability vector of the discrete phase- type distribution. A : matrix, shape (M,M) The transition probability matrix of the discrete 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 = [0.76,0,0.24];
>>> A = [0.34, 0.66, 0; 0.79, 0.05, 0.07; 0.26, 0.73, 0.01];
>>> x = SamplesFromDPH(a, A, 1000);
>>> mt = MarginalMomentsFromTrace(x, 3);
>>> disp(mt);
       26.886       1425.6   1.1352e+05
>>> mp = MomentsFromDPH(a, A, 3);
>>> disp(mp);
       26.995         1398   1.0853e+05

For Mathematica:

>>> a = {0.76,0,0.24};
>>> A = {{0.34, 0.66, 0},{0.79, 0.05, 0.07},{0.26, 0.73, 0.01}};
>>> x = SamplesFromDPH[a, A, 1000];
>>> mt = MarginalMomentsFromTrace[x, 3];
>>> Print[mt];
{27427/1000, 1418939/1000, 111151657/1000}
>>> mp = MomentsFromDPH[a, A, 3];
>>> Print[mp];
{26.995340611502304, 1397.9993695881547, 108525.47866809377}

For Python/Numpy:

>>> a = ml.matrix([[0.76,0,0.24]])
>>> A = ml.matrix([[0.34, 0.66, 0],[0.79, 0.05, 0.07],[0.26, 0.73, 0.01]])
>>> x = SamplesFromDPH(a, A, 1000)
>>> mt = MarginalMomentsFromTrace(x, 3)
>>> print(mt)
[26.765999999999998, 1331.058, 93128.106]
>>> mp = MomentsFromDPH(a, A, 3)
>>> print(mp)
[26.995340611502307, 1397.9993695881547, 108525.47866809377]