Phil Steitz wrote:
1. Either remove or implement the "not implemented yet" distribution persistence methods. I am ambivalent on these, maybe just supporting serialization is enough.
The question is if it happens very often that we obtain data in the form of the EDF. This might be the case if data are pre-processed using different application (or experimental equipment)...
The use case that I had in mind was repeated simulation runs using the same source dataset -- for this it would be handy to be able to digest a large dataset once and then reload just the digest (EDF) for subsequent runs.
I'm thinking about the best form in which EmpiricalDistribution can be
saved,
maybe saving pairs observed_value_i = probability_i
would do the job?
There is more data than that -- remember the bin stats, etc. If we want to do it in a platform-independent way, that will be interesting; otherwise we could just serialize the whole mess using Java serialization (hence the comment that maybe just implementing Serializable is enough).
3. Develop some sort of rationale for the test tolerances. This is an interesting mathstat problem. I would ideally like to use statistical tests (like elsewhere in the random package), but it is not obvious what the right test or test parameters should be.
As long as we test means or variances we can use t test or some variance equality test (Levene test). However we need to choose significane level anyway, so still there is a arbitrary number (like "tolerance" we have now), on the other hand this number have clear interpretation.
Yes, that is the problem. I don't see how exactly we can correctly set df for the t-test, for example, since the sampling distribution of the "mean of EDF-generated values" is sort of an ugly beast that depends on the the number and dispersion of the origial values as well as the number of bins and the number of generated values.
Phil
Piotr
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