Fast approaches for Bayesian estimation of size of hard-to-reach\n populations using Network Scale-up

2018/04/12 by Leonardo Soares Bastos, Bastos, Leonardo S, Natália Santana Paiva +5
Immunology and Microbiology · Mathematics · Medicine · Psychology · #Applications (stat.AP) #Census and Population Estimation #Computation (stat.CO) #Data-Driven Disease Surveillance #FOS: Computer and information sciences #HIV Research and Treatment #HIV, Drug Use, Sexual Risk #Primate Behavior and Ecology

paper · pdf · doi:10.48550/arxiv.1804.04678

openalex publication_date 2018/04/12 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28

Abstract

The Network scale-up method is commonly used to overcome difficulties in\nestimating the size of hard-to-reach populations. The method uses indirect\ninformation based on social network of each participant taken from the general\npopulation, but in some applications a fast computational approach would be\nhighly recommended. We propose a Gibbs sampling method and a Monte Carlo\napproach to sample from the random degree model. We applied the abovementioned\nanalytical strategies to previous data on heavy drug users from Curitiba,\nBrazil.

Citations

• Monte Carlo sampling methods using Markov chains and their applications

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