Resumo

CRUZ REYES, Danna Lesley. Probabilistic Graphical Models Applied to Spatial Analysis in R: Cell Phone Thefts in Bogotá. Tecciencia [online]. 2020, vol.15, n.29, pp.9-22.  Epub 19-Maio-2021. ISSN 1909-3667.  https://doi.org/10.18180/tecciencia.2020.29.2.

Recent technological advances allow large-scale collection, storage and processing information. As a consequence textbf big data has become more important nowadays, since the increase in information has given rise to large and complex data sets that can be potentially exploited to find solutions to relevant problems. This work aims to explain how statistical methods can analyze these large and complex data sets, specifically spatial data. A spatial dependency analysis is carried out by means of a graph that characterizes the spatial structure and a widely used approach known as Conditional Auto-Regressive (CAR). These models are useful for obtaining multivariate joint distributions of a random vector based on univariate conditional specifications. These conditional specifications are based on the Markov properties. Hence, that the conditional distribution of a component of the random vector depends only on a set of neighbors defined by the graph. CAR models are particular cases of random Markov fields. Finally, it is explained how to carry out these analyzes in R language programming including the handling of graphs and the packages used. Finally, the parameters estimation in R is carried out following the Bayesian methodology to data corresponding to stolen cell phones in Bogotá-Colombia.

Palavras-chave : Conditional Auto-Regressive; big data; R programming.

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