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https://issues.apache.org/jira/browse/STATISTICS-77?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17745298#comment-17745298
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Alex Herbert commented on STATISTICS-77:
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{quote}You mean "storeless", don't you?
{quote}
Yes.
However the same algorithms for storeless can be applied to a store of values
too. Having a store does open up the option for more algorithms, such as a sort
and sum. In practice you would not accept the reduced performance of the sort.
But if the values are already known to be sorted then a choice to use a faster
extended precision sum would be helpful. An example is the sum of the
logarithms of a natural sequence to compute the log factorial.
> Implement Sum
> -------------
>
> Key: STATISTICS-77
> URL: https://issues.apache.org/jira/browse/STATISTICS-77
> Project: Commons Statistics
> Issue Type: Sub-task
> Components: descriptive
> Reporter: Anirudh Joshi
> Priority: Minor
> Labels: gsoc, gsoc2023
>
> This implementation uses {{commons-numbers}}
> [Sum|https://github.com/apache/commons-numbers/blob/master/commons-numbers-core/src/main/java/org/apache/commons/numbers/core/Sum.java]
> as the underlying implementation.
> This may be further expanded to include various other Sum implementations
> like [Kahan Sum|https://en.wikipedia.org/wiki/Kahan_summation_algorithm],
> {{Neumaier Sum}} (Two sum method) etc. with the option for the users to
> choose an implementation. However, before beginning the implementation of
> various other algorithms it may be worthwhile to discuss and investigate the
> relative speed of the current Sum implementation compared to
> {{DoubleStream.sum}} through JMH benchmarks.
> When we know the different speeds, it would be more clear if the user should
> have a choice for this statistic, e.g.
> * When using {{DoubleStream.sum}} is too slow and it would be nice to have a
> simple sum variation.
> * When the sum has a lot of cancellation and {{DoubleStream.sum}} suffers
> from random walk around zero.
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