bayesian analysis

Bayesian estimate of population count with false captures: a latent class approach

We propose a capture–recapture model for estimating the size of a population based on multiple lists in presence of out-of-scope units (false captures). Our Bayesian approach makes use of a class of log–linear models with a latent structure. We also address the presence of sources providing partial information implementing a Gibbs Sampler algorithm which generates a sample from the posterior distribution of the population size in the presence of missing data. The proposed method is applied to simulated data sets.

Population size estimation from incomplete multisource lists: A Bayesian perspective on latent class modelling

We propose a capture–recapture model for estimating the size of a population of interest based on a set of administrative sources and/or surveys in the presence of out-of-scope units (false captures). Our Bayesian approach makes use of a certain class of log - linear models with a latent structure. We also address the presence of sources providing partial information implementing a Gibbs Sampler algorithm which generates from the posterior distribution of the population size in presence of missing data. The proposed method is applied to simulated data sets

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