
Open access
Author
Comoglio, Federico
Fracchia, Letizia
Rinaldi, Maurizio
Date
2013-10-07Type
- Journal Article
Abstract
We consider a set of sample counts obtained by sampling arbitrary fractions of a finite volume containing an homogeneously dispersed population of identical objects. We report a Bayesian derivation of the posterior probability distribution of the population size using a binomial likelihood and non-conjugate, discrete uniform priors under sampling with or without replacement. Our derivation yields a computationally feasible formula that can prove useful in a variety of statistical problems involving absolute quantification under uncertainty. We implemented our algorithm in the R package dupiR and compared it with a previously proposed Bayesian method based on a Gamma prior. As a showcase, we demonstrate that our inference framework can be used to estimate bacterial survival curves from measurements characterized by extremely low or zero counts and rather high sampling fractions. All in all, we provide a versatile, general purpose algorithm to infer population sizes from count data, which can find application in a broad spectrum of biological and physical problems. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000073605Publication status
publishedJournal / series
PLoS ONEVolume
Pages / Article No.
Publisher
Public Library of ScienceOrganisational unit
03748 - Paro, Renato (emeritus) / Paro, Renato (emeritus)
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