  We present some new density estimation algorithms obtained by bootstrap
aggregation like Bagging. Our algorithms are analyzed and empirically compared
to other methods found in the statistical literature, like stacking and
boosting for density estimation. We show by extensive simulations that ensemble
learning are effective for density estimation like for classification. Although
our algorithms do not always outperform other methods, some of them are as
simple as bagging, more intuitive and has computational lower cost.
