R_estep: Compute conditional covariance matrix for EM algorithms of...

In awblocker/networkTomography: Tools for Network Tomography

Compute conditional covariance matrix for EM algorithms of Cao et al. (2000)

Description

Computes conditional covariance of OD flows for E-step of EM algorithm from Cao et al. (2000) for their locally IID model.

Usage

R_estep(lambda, phi, A, c, epsilon)

Arguments

lambda	numeric vector (length k) of mean OD flows from last M-step
phi	numeric scalar scale for covariance matrix of xt
A	routing matrix (m x k) for network being analyzed
c	power parameter in model of Cao et al. (2000)
epsilon	numeric nugget to add to diagonal of covariance for numerical stability

Value

conditional covariance matrix (k x k) of OD flows given parameters

References

J. Cao, D. Davis, S. Van Der Viel, and B. Yu. Time-varying network tomography: router link data. Journal of the American Statistical Association, 95:1063-75, 2000.

See Also

Other CaoEtAl: Q_iid(), Q_smoothed(), grad_iid(), grad_smoothed(), locally_iid_EM(), m_estep(), phi_init(), smoothed_EM()

awblocker/networkTomography documentation built on May 14, 2022, 10:05 p.m.