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