def random(mu, lmbda): nu = normal.random(0, 1) y = nu * nu x = mu + (mu * mu * y / (2 * lmbda)) - (mu / (2 * lmbda)) * math.sqrt(4 * mu * lmbda * y + mu * mu * y * y) z = r.random() if (z <= (mu / (mu + x))): return x return mu * mu / x
def random(delta, gmma, xi, lmbda): return lmbda * math.sinh((normal.random(0, 1) - gmma) / delta) + xi
def random(k): avg_=0 for _ in range(k): avg_+=math.pow(normal.random(0,1),2) return avg_
def random(m, S): A = np.asmatrix(la.sqrtm(S)) z = np.zeros(m.shape) for i in range(m.shape[0]): z[i][0] = normal.random(0, 1) return m + np.dot(A, z)
def random(mu, sigma2, lmbda): return normal.random(mu, sigma2) + exponential.random(lmbda)
def random(mu,sigma): return math.exp(normal.random(mu,sigma))
def random(aa, bb): n = normal.random(0, 1) return bb * (aa * n / 2 + math.sqrt((aa * n / 2)**2 + 1))**2
def random(m,s): return abs(normal.random(m,s))
def random(aa, bb, mu, lmbda): n = invgamma.random(aa, bb) return normal.random(mu, math.sqrt(n / lmbda))
def random(): n = ds.rg0() while (n == 1): n = ds.rg0() return normal.random(0, 1) / n
def random(mu, sigma, nu): sum_ = 0 for _ in range(nu): sum_ += normal.random(mu, sigma) return sum_
def random(mu,nu): return (normal.random(0,1)+mu)/math.sqrt(chi2.random(nu)/nu)
def random(a, mu, sigma): n = normal.random(mu, sigma**2) while n < a: n = normal.random(mu, sigma**2) return n
def random(a,b,mu,sigma): n=normal.random(mu,sigma**2) while (n<a or n>b): n=normal.random(mu,sigma**2) return n
def random(delta, gmma, xi, lmbda): v = (normal.random(0, 1) - gmma) / delta return xi + lmbda * math.exp(v)
def random(b,t): w=exponential.random(1/b) return 2*t/(1-t**2)*w+math.sqrt(2/(1-t**2))*math.sqrt(w)*normal.random(0,1)
def random(a, s, sigma): return normal.random(0, sigma) + cauchy.random(a, s)
def random(aa, bb, mu, lmbda): n = gamma.random(aa, bb) return normal.random(mu, math.sqrt(1 / (lmbda * n)))
def random(b, mu, sigma): n = normal.random(mu, sigma**2) while n > b: n = normal.random(mu, sigma**2) return n
def random(t, nu): return normal.random(0, 1) + t * math.sqrt(chi2.random(nu) / nu)
def random(mu, sigma): n = normal.random(mu, sigma) if (n < 0): return 0 return n
def random(s, m): return normal.random(0, s) / math.sqrt(gamma.random(1 / 2, m - 1 / 2))
def random(a, b, nu1, nu2): return (b * normal.random(0, 1) + a * math.sqrt(chi2.random(nu1) / nu1)) / math.sqrt( chi2.random(nu2) / nu2)