Computer Science – Networking and Internet Architecture
Scientific paper
2011-07-20
Computer Science
Networking and Internet Architecture
10 pages, 1 figure
Scientific paper
Loss tomography has received considerable attention in recent years and a number of estimators have been proposed. Although most of the estimators claim to be the maximum likelihood estimators, the claim is only partially true since the maximum likelihood estimate can be obtained at most for a class of data sets. Unfortunately, few people are aware of this restriction that leads to a misconception that an estimator is applicable to all data sets as far as it returns a unique solution. To correct this, we in this paper point out the risk of this misconception and illustrate the inconsistency between data and model in the most influential estimators. To ensure the model used in estimation consistent with the data collected from an experiment, the data sets used in estimation are divided into 4 classes according to the characteristics of observations. Based on the classification, the validity of an estimator is defined and the validity of the most influential estimators is evaluated. In addition, a number of estimators are proposed, one for a class of data sets that have been overlooked. Further, a general estimator is proposed that is applicable to all data classes. The discussion starts from the tree topology and end at the general topology.
No associations
LandOfFree
Fitting a Model to Data in Loss Tomography does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Fitting a Model to Data in Loss Tomography, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Fitting a Model to Data in Loss Tomography will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-34297