Fast approaches for Bayesian estimation of size of hard-to-reach populations using Network Scale-up
Computation
2018-04-16 v1 Applications
Abstract
The Network scale-up method is commonly used to overcome difficulties in estimating the size of hard-to-reach populations. The method uses indirect information based on social network of each participant taken from the general population, but in some applications a fast computational approach would be highly recommended. We propose a Gibbs sampling method and a Monte Carlo approach to sample from the random degree model. We applied the abovementioned analytical strategies to previous data on heavy drug users from Curitiba, Brazil.
Keywords
Cite
@article{arxiv.1804.04678,
title = {Fast approaches for Bayesian estimation of size of hard-to-reach populations using Network Scale-up},
author = {Leonardo S Bastos and Natalia S Paiva and Francisco I Bastos and Daniel A M Villela},
journal= {arXiv preprint arXiv:1804.04678},
year = {2018}
}
Comments
13 pages, 1 figure