m_estep: Compute conditional expectations for EM algorithms of Cao et...

In awblocker/networkTomography: Tools for Network Tomography

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

Description

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

Usage

m_estep(yt, lambda, phi, A, c, epsilon)

Arguments

yt	numeric vector (length m) of link loads from single time
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

numeric vector of same size as lambda with conditional expectations of x

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(), R_estep(), grad_iid(), grad_smoothed(), locally_iid_EM(), phi_init(), smoothed_EM()

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