  We present sparse tree-based and list-based density estimation methods for
binary/categorical data. Our density estimation models are higher dimensional
analogies to variable bin width histograms. In each leaf of the tree (or list),
the density is constant, similar to the flat density within the bin of a
histogram. Histograms, however, cannot easily be visualized in more than two
dimensions, whereas our models can. The accuracy of histograms fades as
dimensions increase, whereas our models have priors that help with
generalization. Our models are sparse, unlike high-dimensional fixed-bin
histograms. We present three generative modeling methods, where the first one
allows the user to specify the preferred number of leaves in the tree within a
Bayesian prior. The second method allows the user to specify the preferred
number of branches within the prior. The third method returns density lists
(rather than trees) and allows the user to specify the preferred number of
rules and the length of rules within the prior. The new approaches often yield
a better balance between sparsity and accuracy of density estimates than other
methods for this task. We present an application to crime analysis, where we
estimate how unusual each type of modus operandi is for a house break-in.
