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1 ribution associated with peak matches into a multinomial distribution.
2 ations to the pattern expected from a random multinomial distribution.
3 ations to the pattern expected from a random multinomial distribution.
4 the inadequacy of the very popular Dirichlet-multinomial distribution.
5                                The Dirichlet-multinomial distribution allows the analyst to calculate
6 hat K-means cluster sizes generally follow a multinomial distribution and the failure probability of
7 quence read counts by a mixture of Dirichlet-multinomial distributions and explicitly accounts for ce
8       Ambiguous read mapping is modeled as a multinomial distribution, and ambiguous reads are assign
9 ompound using Monte Carlo simulation via the multinomial distribution, and calculates the isotopic en
10 produced, which closely matched a calculated multinomial distribution based on IBC clonality.
11 ther, all four are better fitted by zero-sum multinomial distributions, characteristic of Hubbell's n
12 arity quantification method based on product multinomial distributions, demonstrate its ability to id
13 nce, and viability whose parameters define a multinomial distribution for single-spore data.
14                                    Under the multinomial distribution for the read counts and a prior
15                                            A multinomial distribution likelihood is constructed by co
16            In the present work, we propose a multinomial distribution model for assessment of Ag sele
17                                Using a quasi-multinomial distribution model, our method is able to ca
18 ts from potential candidate peptides using a multinomial distribution model.
19 --using a heuristic algorithm, which matches multinomial distributions of distinct viral variants ove
20 f internal categories, each characterized by multinomial distributions over words (in abstracts) and
21 depth for all data as a mixture of Dirichlet-multinomial distributions, resulting in significant impr
22 framework region and CDR codons coupled with multinomial distribution studies found no substantial ev
23  this work, we provide a method based on the multinomial distribution that identifies signals of disp
24           Hence, instead of the conventional multinomial distribution, these tables have the empirica
25 ted with any observed sample, against a null multinomial distribution, using the likelihood-ratio sta
26          The DMN distribution reduces to the multinomial distribution when the overdispersion paramet
27 new regression model combining the Dirichlet-multinomial distribution with recursive partitioning pro
28  within a community should follow a zero-sum multinomial distribution (ZSM), but this has not, so far