def test_GammaProcess_symbolic():
t, d, x, y, g, l = symbols('t d x y g l', positive=True)
X = GammaProcess("X", l, g)
raises(NotImplementedError, lambda: X[t])
raises(IndexError, lambda: X(-1))
assert isinstance(X(t), RandomIndexedSymbol)
assert X.state_space == Interval(0, oo)
assert X.distribution(t) == GammaDistribution(g * t, 1 / l)
assert X.joint_distribution(5, X(3)) == JointDistributionHandmade(
Lambda(
(X(5), X(3)),
l**(8 * g) * exp(-l * X(3)) * exp(-l * X(5)) * X(3)**(3 * g - 1) *
X(5)**(5 * g - 1) / (gamma(3 * g) * gamma(5 * g))))
# property of the gamma process at any given timestamp
assert E(X(t)) == g * t / l
assert variance(X(t)).simplify() == g * t / l**2
# Equivalent to E(2*X(1)) + E(X(1)**2) + E(X(1)**3), where E(X(1)) == g/l
assert E(X(t)**2 + X(d)*2 + X(y)**3, Contains(t, Interval.Lopen(0, 1))
& Contains(d, Interval.Lopen(1, 2)) & Contains(y, Interval.Ropen(3, 4))) == \
2*g/l + (g**2 + g)/l**2 + (g**3 + 3*g**2 + 2*g)/l**3
assert P(X(t) > 3, Contains(t, Interval.Lopen(3, 4))).simplify() == \
1 - lowergamma(g, 3*l)/gamma(g) # equivalent to P(X(1)>3)
#test issue 20078
assert (2 * X(t) + 3 * X(t)).simplify() == 5 * X(t)
assert (2 * X(t) - 3 * X(t)).simplify() == -X(t)
assert (2 * (0.25 * X(t))).simplify() == 0.5 * X(t)
assert (2 * X(t) * 0.25 * X(t)).simplify() == 0.5 * X(t)**2
assert (X(t)**2 + X(t)**3).simplify() == (X(t) + 1) * X(t)**2
def marginal_distribution(self, indices, *sym):
if len(indices) == 2:
return self.pdf(*sym)
if indices[0] == 0:
#For marginal over `x`, return non-standardized Student-T's
#distribution
x = sym[0]
v, mu, sigma = self.alpha - S(1)/2, self.mu, \
S(self.beta)/(self.lamda * self.alpha)
return Lambda(sym, gamma((v + 1)/2)/(gamma(v/2)*sqrt(pi*v)*sigma)*\
(1 + 1/v*((x - mu)/sigma)**2)**((-v -1)/2))
#For marginal over `tau`, return Gamma distribution as per construction
from sympy.stats.crv_types import GammaDistribution
return Lambda(sym, GammaDistribution(self.alpha, self.beta)(sym[0]))
