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By using Petri net unfolding techniques and by extracting the process model of each object from the entire process model, the sequencing analysis for operations in supply chain processes becomes possible.
To test the accuracy of SNP calling, we developed a statistical method to model the sequencing error distribution.
The discrete nature of count data led researchers to model the sequencing data using Poisson distribution (see e.g. Marioni et al. 2008).
As a result, our model uses 39 positional weight parameters and 16 stacking energy parameters and 1 constant totally 39 + 16 + 1 = 56 parameters, which is highly dependent on N, to model the sequencing preference of a given nucleotide.
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There have also been some attempts to model the sequence of events before the incident, and the consequences of the resulting fires and explosions.
A numerical procedure has been developed to model the sequences of failure which can occur within steel beam-to-column connections under fire conditions.
A hidden Markov model is used to model the sequence of observations of individual modalities.
So no Borel normal sequence, and hence no random sequence, can model the sequence of outcomes of a Markov chain, even though each outcome happens by chance.
In this new model, the sequence-dependent setup time is used instead of the sequence-independent setup time considered in the traditional model.
The goal of the word-based language model, is to model the sequence of words in the context of the task, being performed by the speech recognition system.
This combined with weight matrix based binding models [20] and spacing constraints [9], [10] gives us quantitative tools to model the sequence basis of eukaryotic transcriptional regulation.
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