sdeint/wiener.py
def _sigmainf(N, h, m, dW, Km0, Pm0):
    """Asymptotic covariance matrix \Sigma_\infty  Wiktorsson2001 eqn (4.5)"""
    M = m*(m-1)/2
    Im = broadcast_to(np.eye(m), (N, m, m))
    IM = broadcast_to(np.eye(M), (N, M, M))
    Ims0 = np.eye(m**2)
    factor1 = broadcast_to((2.0/h)*np.dot(Km0, Ims0 - Pm0), (N, M, m**2))
    factor2 = _kp2(Im, _dot(dW, _t(dW)))
    factor3 = broadcast_to(np.dot(Ims0 - Pm0, Km0.T), (N, m**2, M))
    return 2*IM + _dot(_dot(factor1, factor2), factor3)
Asymptotic covariance matrix \Sigma_\infty  Wiktorsson2001 eqn (4.5)
Asymptotic covariance matrix \Sigma_\infty  Wiktorsson2001 eqn (4.5)
Asymptotic covariance matrix \ Sigma_ \ infty Wiktorsson2001 eqn ( 4 . 5 )
def _sigmainf(N, h, m, dW, Km0, Pm0):
    M = m*(m-1)/2
    Im = broadcast_to(np.eye(m), (N, m, m))
    IM = broadcast_to(np.eye(M), (N, M, M))
    Ims0 = np.eye(m**2)
    factor1 = broadcast_to((2.0/h)*np.dot(Km0, Ims0 - Pm0), (N, M, m**2))
    factor2 = _kp2(Im, _dot(dW, _t(dW)))
    factor3 = broadcast_to(np.dot(Ims0 - Pm0, Km0.T), (N, m**2, M))
    return 2*IM + _dot(_dot(factor1, factor2), factor3)
def _sigmainf ( N , h , m , dW , Km0 , Pm0 ) : M = m * ( m - 1 ) / 2 Im = broadcast_to ( np . eye ( m ) , ( N , m , m ) ) IM = broadcast_to ( np . eye ( M ) , ( N , M , M ) ) Ims0 = np . eye ( m ** 2 ) factor1 = broadcast_to ( ( 2.0 / h ) * np . dot ( Km0 , Ims0 - Pm0 ) , ( N , M , m ** 2 ) ) factor2 = _kp2 ( Im , _dot ( dW , _t ( dW ) ) ) factor3 = broadcast_to ( np . dot ( Ims0 - Pm0 , Km0 . T ) , ( N , m ** 2 , M ) ) return 2 * IM + _dot ( _dot ( factor1 , factor2 ) , factor3 )
Asymptotic covariance matrix \Sigma_\infty  Wiktorsson2001 eqn (4.5)
Asymptotic covariance matrix \ Sigma_ \ infty Wiktorsson2001 eqn ( 4 . 5 )
"""Asymptotic covariance matrix \Sigma_\infty  Wiktorsson2001 eqn (4.5)"""
N
h
m
dW
Km0
Pm0