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

Description Usage Arguments Value References See Also

View source: R/caoEtAl.R

Description

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

Usage

1
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


networkTomography documentation built on May 29, 2017, 4:56 p.m.