def testRandom(self):
from scipy.special import kv
from numpy import sqrt
a = 2.
b = 1.
p = 1
gig = GeneralizedInverseGaussian(a, b, p)
samples = gig.random(10000)
mu_analytical = sqrt(b) * kv(p + 1, sqrt(a * b)) / (sqrt(a) * kv(p, sqrt(a * b)))
var_analytical = b * kv(p + 2, sqrt(a * b)) / a / kv(p, sqrt(a * b)) - mu_analytical ** 2
mu = numpy.mean(samples)
var = numpy.var(samples)
self.assertAlmostEqual(mu_analytical, mu, delta=1e-1)
self.assertAlmostEqual(var_analytical, var, delta=1e-1)
def testRandom(self):
from scipy.special import kv
from numpy import sqrt
a = 2.
b = 1.
p = 1
gig = GeneralizedInverseGaussian(a, b, p)
samples = gig.random(10000)
mu_analytical = sqrt(b) * kv(p + 1, sqrt(
a * b)) / (sqrt(a) * kv(p, sqrt(a * b)))
var_analytical = b * kv(p + 2, sqrt(a * b)) / a / kv(p, sqrt(
a * b)) - mu_analytical**2
mu = numpy.mean(samples)
var = numpy.var(samples)
self.assertAlmostEqual(mu_analytical, mu, delta=1e-1)
self.assertAlmostEqual(var_analytical, var, delta=1e-1)
