dobj.dxt.tomogravity

length-k numeric vector of point-to-point
  flows

length-m numeric vector of observed aggregate
  flows

list of source and destination flow
  indices corresponding to each point-to-point flow, as
  produced by <code><a rd-options="" href="/link/getSrcDstIndices?package=networkTomography&version=0.3" data-mini-rdoc="networkTomography::getSrcDstIndices">getSrcDstIndices</a></code>

srcDstInd

regularization parameter for mutual
  information prior. Note that this is scaled by the
  squared total traffic in the objective function before
  scaling the mututal information prior.

lambda


Requires bounded optimization to maintain positive OD
flows, and only those flows that are not deterministically
zero should be included in the estimation.


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.

dobj.dxt.tomogravity: Analytic gradient of objective function of Zhang et al. 2003

Description

Usage

Arguments

Value