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Thus, the proposed model is error resilient and with high robustness.
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The development and maintenance of feature models are error-prone and time-consuming tasks, especially considering industrial-size models with thousands of features.
The last variable in the model is the error term.
The assumption of the model is that error terms are independent across time, but may have cross-equation contemporaneous correlations.
The only difference between the FASTA model and the true mixed model is the error in variance components since they were estimated with a pure random model.
The Gilbert/Elliot error model is a valuable error model still considered as a reasonable solution for modeling of errors in wireless links [34, 37].
The error model is based on mappings (error curves) that assign error rates to specific base positions.
The first model is a stochastic error (SE) model, where the CSIT error is Gaussian-distributed.
The sequencing error model is set according to the error profile of 80 bps Illumina reads.
A separate normal sizing error model is used for fragment sizing error for small restriction fragments below a specified threshold.
An approach to evaluate a language model is word recognition error rate [60].
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