Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extrapolation of Urn Models via Poissonization: Accurate Measurements of
  the Microbial Unknown   The availability of high-throughput parallel methods for sequencing microbial
communities is increasing our knowledge of the microbial world at an
unprecedented rate. Though most attention has focused on determining
lower-bounds on the alpha-diversity i.e. the total number of different species
present in the environment, tight bounds on this quantity may be highly
uncertain because a small fraction of the environment could be composed of a
vast number of different species. To better assess what remains unknown, we
propose instead to predict the fraction of the environment that belongs to
unsampled classes. Modeling samples as draws with replacement of colored balls
from an urn with an unknown composition, and under the sole assumption that
there are still undiscovered species, we show that conditionally unbiased
predictors and exact prediction intervals (of constant length in logarithmic
scale) are possible for the fraction of the environment that belongs to
unsampled classes. Our predictions are based on a Poissonization argument,
which we have implemented in what we call the Embedding algorithm. In fixed
i.e. non-randomized sample sizes, the algorithm leads to very accurate
predictions on a sub-sample of the original sample. We quantify the effect of
fixed sample sizes on our prediction intervals and test our methods and others
found in the literature against simulated environments, which we devise taking
into account datasets from a human-gut and -hand microbiota. Our methodology
applies to any dataset that can be conceptualized as a sample with replacement
from an urn. In particular, it could be applied, for example, to quantify the
proportion of all the unseen solutions to a binding site problem in a random
RNA pool, or to reassess the surveillance of a certain terrorist group,
predicting the conditional probability that it deploys a new tactic in a next
attack.

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The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: eess
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The categories of this paper are: gr-qc