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From Phil Steitz <>
Subject Re: [math] MATH-224 - need a better idea
Date Mon, 20 Apr 2009 11:01:20 GMT
Ted Dunning wrote:
> That is a fine answer for some things, but the parallel cases fail.
> My feeling is that there are a few cases where there are nice aggregatable
> summary statistics like moments and there are many cases where this just
> doesn't work well (such as rank statistics). 
Yes, this is why not all statistics are "storeless."  We have another 
"summary" class that maintains its data in storage and supports 
"rolling" behavior in DescriptiveStatistics.  The discussion here is 
focussed on the "storeless" case, which is limited to those stats that 
are computable in this way.  The cases of interest are stats that can be 
computed in one pass through the data but which can't be "aggregated" 
post hoc.  John's approach provides a simple solution to this problem.

For completeness, we should probably similarly implement aggregation in 
the sense defined in MATH-224 for DescriptiveStatistics as well. 

>  For the latter, case I usually
> make do with a surrogate such as a random sub-sample or a recency weighted
> random sub-sample combined with a few aggregatable stats such as total
> samples, max, min, sum and second moment.  That gives me most of what I want
> and if the sub-sample is reasonably large, I can sometimes estimate a few
> parameters such as total uniques.  The sub-sampled data streams can be
> combined trivially so I now have a aggregatable approximation of
> non-aggregatable statistics.  For descriptive quantiles this is generally
> just fine.
> On Sun, Apr 19, 2009 at 2:44 PM, John Bollinger <> wrote:
>> The key would be to generate the aggregate statistics at the same time as
>> the per-partition ones, instead of aggregating them after the fact.

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