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Negociando a vida e a morte: estado, igreja e crime em uma favela de Belo Horizonte
(Universidade Federal de São Carlos, 2020-04-14) Beraldo, Ana; Feltran, Gabriel de Santis; https://lattes.cnpq.br/6789864439048444; https://lattes.cnpq.br/4878397484815635
This thesis deals with dynamics of life and death at the margins. Based on fieldwork carried out in a large slum in the city of Belo Horizonte, here called Morro da Luz, an analysis is made about the coexistence of different regulatory regimes in territories in the urban periphery, that is, the interaction between different logics, morally informed, to organize symbolically and materially the relations of sociability and the classifications of what is or is no longer socially acceptable, desired or disapproved. Throughout the text, it is demonstrated that, currently, there are three main normative regimes operating in Luz and, possibly, in other similar territories throughout Latin America: state (in its multiple and often antagonistic facets); crime (which, in Belo Horizonte, is characterized by the presence of several small groups that compete with each other, but which are still capable of building a criminal discourse unit); and church (especially the many evangelical strands). These three normativities build symbolic relationships among themselves that alternate between dispute and articulation, forming a normative triangulation that acts, based on the sharing of the appreciation of the notion of respectability, as a mechanism for the daily moral analysis of people and behaviors. These dynamics are made possible by a broader scenario, typical of post-colonial nations, of production and maintenance of economic and rights inequalities, which, in the favela, make subjects in an eternal negotiation between life and death.
Modelo de mistura de regressão: uma abordagem bayesiana
(Universidade Federal de São Carlos, 2020-04-14) Cotrim, Luiz Gabriel Fernandes; Zuanetti, Daiane Aparecida; https://lattes.cnpq.br/8352484284929824; https://lattes.cnpq.br/1185342603980646
In the current dissertation, we study the mixture regression models and present two Bayesian methodologies for their estimation. The first one considers the number of components is known and we propose the use of two Bayesian model selection criteria, DIC and EBIC, to identify the number of components. In the other one, we propose a reversible jump algorithm with splitmerge steps that estimates parameters and the number of components. We apply the proposed methodologies and also the EM algorithm, already available in R package, for simulated dataset and for Brazilian educational data, studying the relationship among the Basic Education Development Index and some socioeconomic and demographic data.