@ -19,9 +19,18 @@ Wicked Fast, Accurate Quantiles Using 't-Digests'
## Description
The t-digest construction algorithm uses a variant of 1-dimensional k-means clustering to produce a very compact data structure that allows accurate estimation of quantiles. This t-digest data structure can be used to estimate quantiles, compute other rank statistics or even to estimate related measures like trimmed means. The advantage of the t-digest over previous digests for this purpose is that the t-digest handles data with full floating point resolution. With small changes, the t-digest can handle values from any ordered set for which we can compute something akin to a mean. The accuracy of quantile estimates produced by t-digests can be orders of magnitude more accurate than those produced by previous digest algorithms.
See [the original paper by Ted Dunning](https://raw.githubusercontent.com/tdunning/t-digest/master/docs/t-digest-paper/histo.pdf) for more details on t-Digests.
The t-Digest construction algorithm uses a variant of 1-dimensional
k-means clustering to produce a very compact data structure that allows
accurate estimation of quantiles. This t-Digest data structure can be used
to estimate quantiles, compute other rank statistics or even to estimate
related measures like trimmed means. The advantage of the t-Digest over
previous digests for this purpose is that the t-Digest handles data with
full floating point resolution. The accuracy of quantile estimates produced
by t-Digests can be orders of magnitude more accurate than those produced
by previous digest algorithms. Methods are provided to create and update
t-Digests and retreive quantiles from the accumulated distributions.
See [the original paper by Ted Dunning & Otmar Ertl](https://arxiv.org/abs/1902.04023) for more details on t-Digests.