Sentence examples for binomial mixture models to from inspiring English sources

We have described two statistical models based on Binomial mixture models to infer SNVs from aligned NGS data obtained from tumors.

We have shown how to use binomial mixture models to estimate microbial core- and pan-genome size, and the vast literature on capture-recapture methods should be further exploited in microbial pangenomics, as it has been in closely related fields like metagenomics [ 21].

Analyzing pan-genomics data with binomial mixture models is a way to handle dependencies between genomes, which we find is always present.

Using both pre- and post-harvest data, abundance estimates were acquired using binomial mixture models.

Figure 4 can be viewed as a graphical display of the binomial mixture models.

In this perspective, the binomial mixture model will tend to under-estimate the true pan-size for smaller data sets.

Therefore, using four waves of the Survey of Health, Ageing and Retirement in Europe (SHARE), this study applies the correlated random effects method to the negative binomial and finite mixture models to analyse the extent to which the interaction of cognitive and sensory impairments is associated with health care use.

Results: We developed three implementations of a probabilistic Binomial mixture model, called SNVMix, designed to infer SNVs from NGS data from tumors to address this problem.

As an alternative to the binomial mixture model estimate, we have also included the Chao lower-bound estimate [ 11] when fitting to real data.

With the statistical viewpoint, this problem is equivalent to the parameter estimation for an infinite binomial mixture model.

To overcome these limitations, we propose a probabilistic approach based on a Binomial mixture model, called SNVMix1, which computes posterior probabilities, providing a measure of confidence on the SNV predictions.