grad_iid

numeric vector (length k+1) of
  log(lambda) (1:k) and log(phi) (last entry)

logtheta

power parameter in model of Cao et al. (2000)

matrix (n x k) of conditional expectations for
  OD flows, one time per row

numeric vector (length k) containing
  diagonal of conditional covariance matrix R

rdiag

numeric nugget to add to diagonal of
  covariance for numerical stability

epsilon


Computes gradient of Q-function with respect to
log(c(lambda,phi)) for EM algorithm from Cao et al. (2000)
for their locally IID model.


networkTomography implements the methods developed and evaluated in
Blocker and Airoldi (2011) and Airoldi and Blocker (2012). These include the
authors' own dynamic multilevel model with calibration based upon a Gaussian
state-space model in addition to implementations of the methods of Tebaldi &
West (1998; Poisson-Gamma model with MCMC sampling), Zhang et al. (2002;
tomogravity), Cao et al. (2000; Gaussian model with mean-variance relation),
and Vardi (1996; method of moments). Data from the 1router network of Cao et
al. (2000), the Abilene network of Fang et al. (2007), and the CMU network
of Blocker and Airoldi (2011) are included for testing and reproducibility.

grad_iid: Compute analytic gradient of Q-function for locally IID EM algorithm of Cao et al. (2000)

Description

Usage

Arguments

Value

References

See Also