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From a mathematical point of view, the problem of estimating network reliability belongs to the #P complexity class.
The techniques for estimating network capacity are improved with three extra approaches, i.e., optimistic, realistic and pessimistic ones, which approximate the model to a real network operation.
Two well-known techniques for estimating network averages (nu (G)) are the Metropolis–Hastings MCMC (MH-MCMC) scheme [1, 9, 13, 14] and Respondent-Driven sampling (RDS) [6 8].
Our basic approach was to simulate DNA sequences using the neutral coalescent with and without recombination [see 14] and then run the resulting alignments through a variety of algorithms for estimating network relationships among sequences (Figure 1).
Appropriate low-level analysis of profiling data is known to be important [ 57] so estimating network models from suitably processed data would improve both their accuracy and reliability.
Specifically, the majority of network-based methods focus on estimating network information from gene-expression data using probabilistic analyses, such as clustering, to infer co-regulated genes from co-expressed genes [ 41].
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It should be noted that the formulation for SCED is primarily based on the estimated network topology by the state estimator in EMS.
The aim of this problem is to estimate network capabilities.
Moreover, the target network utilization is dynamically varied based on the estimated network load.
A new flow is admitted if the aggregated traffic load is less than the estimated network capacity.
The new Speed Labels feature is one of the more compelling of the bunch, offering estimated network signals prior to logging on.
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