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There are about thirty planktonic species in all, and each thrives at a different temperature, so that by counting a species' prevalence in a given sample it is possible to estimate the ocean temperatures at the time the sediment was formed.
Furthermore, when multiple DNA copy number changes are present in a sample it is possible to estimate the degree of stromal contamination present in a given tumour biopsy, with the aid of SiDCoN.
After doing this for each child in a sample, it is possible to estimate a mean-constant curve using the mean values of the parameter estimates, thereby characterizing average growth in the sample.
Nonetheless, from the frequencies of expression found in a given sample, it is possible to estimate the total number of genes that are actually expressed in a transcriptome, even if their expression was not detected in the particular sample.
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Nevertheless, for large samples, it is possible to estimate the rarefied p from the observed distribution by implicitly removing a given fraction of the underlying mutations.
Now, since we have the samples of the noise ν(t), it is possible to estimate its variance given the samples.
The graph can also be used in the reverse manner: given q, it is possible to estimate the required sample size in order to achieve a desired power.
Thus, it is possible to estimate the PATT with the matched samples while using sampling weights.
From theoretical assumptions of the collection efficiency and theoretical droplet spectra it is possible to estimate the liquid water content of the sampled fog or cloud.
However, even though we cannot calculate the test error, it is possible to estimate the expected test error using training samples.
With the values of K a and I R derived from the experimental data, it is possible to estimate the allele frequencies in the pooled samples.
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