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All is well and good if we content ourselves with computations that can be distributed efficiently such as summing hourly revenue to produce daily revenue, or calculating click-through rates. In the language of Gray et al., the former calculation is distributive: we can sum the raw event prices to produce hourly revenue over each combination of dimensions and in turn sum this intermediary for furt
JDistlib—Java Statistical Distribution Library
A Java package that provides routines for various statistical distributions. Main Features Computation of the density (pdf), cumulative (cdf), quantile, and random variates of many popular statistical distributions, such as Ansari-Bradley, Beta, Binomial, Cauchy, Chi square, Exponential, Fisher's F, Gamma, Geometric, Hypergeometric, Kendall, Logistic, Log normal, Negative binomial, Noncentral beta
分位数・中央値
分位数・中央値 概要 昇順に整列された q 個の値に対して、その分布を m 等分する時、 1 * q/m , 2 * q/m, 3 * q/m, ..., i * q/m, ..., (m - 2) * q/m, (m - 1) * q/m 番目のそれぞれの値を分位数(quantile)と言う（分位点、分位値、クォンタイルとも言う）。 i 番目の分位点は「第 i m 分位数」と呼ぶ。m には4を用いることが多く、四分位数(quartile, hinge)は、第 i ヒンジとも言う。ほかに、 三分位数(tertile)、五分位値(quintile)、十分位数(decile)、百分位数／パーセント点／パーセンタイル(percentile)を用いることがある。第 i 百分位数は下側 i パーセント点と言い、第(100 － i)百分位数を上側 i パーセント点という。 特別な分位点 中央値:
Q-Digest | paper cruncher
Continuing on the same note as the Greenwald-Khanna post, we discuss another novel data structure for order statistics called Q-Digest. Compared to GK, Q-Digest is much simpler, easier to implement and extends to a distributed solution very easily. The data structure was designed with sensor networks in mind, where minimizing radio transmission overhead is of paramount importance. It originally a
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Streaming histograms... faster
Database Performance Monitor (DPM) | SolarWinds
GitHub - tdunning/t-digest: A new data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means
Microsoft PowerPoint - lecture13 [Compatibility Mode]
