  Divergences or similarity measures between probability distributions have
become a very useful tool for studying different aspects of statistical objects
such as time series, networks and images. Notably not every divergence provides
identical results when applied to the same problem. Therefore it is convenient
to have the widest possible set of divergences to be applied to the problems
under study. Besides this choice an essential step in the analysis of every
statistical object is the mapping of each one of their representing values into
an alphabet of symbols conveniently chosen. In this work we attack both
problems, that is, the choice of a family of divergences and the way to do the
map into a symbolic sequence. For advancing in the first task we work with the
family of divergences known as the Burbea-Rao centroids (BRC) and for the
second one we proceed by mapping the original object into a symbolic sequence
through the use of ordinal patterns. Finally we apply our proposals to analyse
simulated and real time series and to real textured images. The main conclusion
of our work is that the best BRC, at least in the studied cases, is the Jensen
Shannon divergence, besides the fact that it verifies some interesting formal
properties.
