essHist:The Essential Histogram
Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of
multiscale variational inference. In other words, the resulting
histogram servers as an optimal density estimator, and
meanwhile recovers the features, such as increases or modes,
with both false positive and false negative controls. Moreover,
it provides a parsimonious representation in terms of the
number of blocks, which simplifies data interpretation. The
only assumption for the method is that data points are
independent and identically distributed, so it applies to
fairly general situations, including continuous distributions,
discrete distributions, and mixtures of both. For details see
Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.
