  In this paper, we propose ISDE (Independence Structure Density Estimation),
an algorithm designed to estimate a multivariate density under Kullback-Leibler
loss and the Independence Structure (IS) model. IS tackles the curse of
dimensionality by separating features into independent groups. We explain the
construction of ISDE and present some experiments to show its performance on
synthetic and real-world data. Performance is measured quantitatively by
comparing empirical $\log$-likelihood with other density estimation methods and
qualitatively by analyzing outputted partitions of variables. We also provide
information about complexity and running time.
