Bayesian Inference from Count Data Using Discrete Uniform Priors
| LEADER | naa a22 4500 | ||
|---|---|---|---|
| 001 | 528785192 | ||
| 005 | 20180924065518.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 180924e20131007xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.3929/ethz-b-000073605 |2 doi |
| 024 | 7 | 0 | |a 10.1371/journal.pone.0074388 |2 doi |
| 035 | |a (ETHRESEARCH)oai:www.research-collecti.ethz.ch:20.500.11850/73605 | ||
| 100 | 1 | |a Comoglio |D Federico | |
| 245 | 1 | 0 | |a Bayesian Inference from Count Data Using Discrete Uniform Priors |h [Elektronische Daten] |c [Federico Comoglio, Letizia Fracchia, Maurizio Rinaldi] |
| 246 | 0 | |a PLoS ONE | |
| 506 | |a Open access |2 ethresearch | ||
| 520 | 3 | |a 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. | |
| 540 | |a Creative Commons Attribution 3.0 Unported |u http://creativecommons.org/licenses/by/3.0 |2 ethresearch | ||
| 700 | 1 | |a Fracchia |D Letizia |e joint author | |
| 700 | 1 | |a Rinaldi |D Maurizio |e joint author | |
| 773 | 0 | |t PLoS ONE |d San Francisco, CA, USA : Public Library of Science |g 8 (10), p. e74388 |x 1932-6203 | |
| 856 | 4 | 0 | |u http://hdl.handle.net/20.500.11850/73605 |q text/html |z WWW-Backlink auf das Repository (Open access) |
| 908 | |D 1 |a Journal Article |2 ethresearch | ||
| 950 | |B ETHRESEARCH |P 856 |E 40 |u http://hdl.handle.net/20.500.11850/73605 |q text/html |z WWW-Backlink auf das Repository (Open access) | ||
| 950 | |B ETHRESEARCH |P 100 |E 1- |a Comoglio |D Federico | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Fracchia |D Letizia |e joint author | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Rinaldi |D Maurizio |e joint author | ||
| 950 | |B ETHRESEARCH |P 773 |E 0- |t PLoS ONE |d San Francisco, CA, USA : Public Library of Science |g 8 (10), p. e74388 |x 1932-6203 | ||
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 949 | |B ETHRESEARCH |F ETHRESEARCH |b ETHRESEARCH |j Journal Article |c Open access | ||