Non-linear bayesian model applied to the population’s prediction of states of Brazil

• Autor(a) principal: Barros, Kleber Napoleão Nunes de Oliveira
• Data de Publicação: 2020
• Outros Autores: Albuquerque, Mácio Augusto de, Fernandes, Maria da Conceição Lacerda
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Research, Society and Development
• Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/5558
• topic: Inferencia bayesiana; Crecimiento de la población; Curvas de crecimiento.; Inferência Bayesiana; Crescimento Populacional; Curvas de Crescimento.; Bayesian Inference; Populational growth; Growth curves.

The data are adjusted through a non-linear combination of parameters in non-linear models. Bayesian inference is an important tool that can be applied to this type of model. Growth data is essentially non-linear, thus making it possible to use this technique in your analysis; given that the Bayesian theory has a great advantage of providing the prediction of probabilities in a direct way. Brazilian municipalities receive federal government resources based on demographic statistical data collected every ten years by the Brazilian Institute of Geography and Statistics (IBGE), as well as surveys carried out periodically by sampling in households, thus obtaining annual information on demographic characteristics and socioeconomic status of the population called the National Household Sample Survey (PNAD). The objective of this work is to estimate the Brazilian population growth in the states and federal district for the years 2016 and 2020, based on the demographic results of the Censuses for the years 1991, 2000, 2010 and 2012; making use of an asymptotic model, the exponential with three parameters. The Bayesian model was used to estimate the parameters. With the application of such techniques, it was possible to obtain predictions of changes in the Brazilian population contingent by state, for the respective years (2016 and 2020). The North and Midwest regions showed a significant increase in their populations. Lower population rates were seen in coastal regions.