"network estimation" is a correct and usable phrase in written English.
It is commonly used in the field of network analysis and refers to the process of estimating the connections and relationships between different elements in a network. Example: The goal of the study was to use network estimation techniques to analyze the social interactions among students in the classroom.
Number of children, parents and all edges were estimated by the SiGN-BN Bayesian network estimation program (http://sign.hgc.jp/signbn/index.html ) [ 76 ].
Next, we estimate the transmission time of this aggregated packet assuming that the previous network estimation (6) will be usable over the time interval.
For this purpose, we estimated a probabilistic network of relationships between these genes using a Bayesian network estimation program, SiGN-BN [ 9, 10], implemented on the supercomputer system at Human Genome Center, The Institute of Medical Science, The University of Tokyo [ 11].
In addition, another artificial neural network estimation approach with a single back-propagation network, called FAPSO-SBP, is also established.
A potential hazardous station was identified from the neural network estimation, which exhibited a significantly higher acceleration than that of the design value.
Regression analysis and artificial neural network estimation indicated that there are acceptable correlations between deformation modulus and coefficient of subgrade reaction and these parameters.
There were good overlap between network estimations and unseen data.
The network estimations are compared with data collected from flat and structural rolling mills.
Eighty three per cent of network estimations show below 5% error.
The test results showed that the coefficient of determination and mean absolute percentage error between the optimized artificial neural network estimations and measured values for testing datasets are higher than 99% and %5, respectively.
This paper addresses the problem of distributed in-network estimation for a vector of interest, which is sparse in nature.
