@article{Cruz Reyes_2020, title={Probabilistic Graphical Models Applied to Spatial Analysis in R: Cell Phone Thefts in Bogotá}, volume={15}, url={https://tecciencia.ecci.edu.co/index.php/TECCIENCIA/article/view/28}, abstractNote={<p>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<br>to large and complex data sets that can be potentially exploited to find solutions to relevant problems. This<br>work aims to explain how statistical methods can analyze these large and complex data sets, specifically spatial<br>data. A spatial dependency analysis is carried out by means of a graph that characterizes the spatial structure<br>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 uni-variate conditional specifications. These<br>conditional specifications are based on the Markov properties. Hence, that the conditional distribution of a<br>component of the random vector depends only on a set of neighbors defined by the graph. CAR models are<br>particular cases of random Markov fields. Finally, it is explained how to carry out these analyzes in R language<br>programming including the handling of graphs and the packages used. Finally, the parameters estimation in<br>R is carried out following the Bayesian methodology to data corresponding to stolen cell phones in BogotáColombia.</p>}, number={29}, journal={Tecciencia}, author={Cruz Reyes, Danna Lesley}, year={2020}, month={Dec.}, pages={9–22} }