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Methods for working with distribution support added for univariate distributions #149

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merged 4 commits into from
Sep 20, 2013

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ingmarschuster
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This is the first batch of implementations to fix #126. Please review carefully (repetetive work, easy to make mistakes).

Wasn't sure about EdgeworthAbstract distribution.



#min(d::Beta) = zero(Real) # maybe inf(::Beta) = 0 would make sense
#max(d::Beta) = one(Real) # and sup(::Beta) = 1
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The consensus in #150 seems to be use min/max to get infimum and supremum even when the domain is open. what about uncommenting these functions (as well as other commented out min/max)?

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Yes, let's use min and max. Do zero(Real) and one(Real) exist? I think only zero(Float64) and one(Float64) will work.

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I tested for the existance of zero(Real) and one(Real) on the julia command line, gave the expected results.

@lindahua
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I believe this is largely ready, except several minor things:

  • In Julia, zero(Real) and one(Real) produce int(0) and int(1) -- this is what I got in latest Julia. The document has made it clear that for continuous distributions, the value type is Float64. Therefore, it might better to simply use 0.0 and 1.0 here.
  • Would you please look into the Travis failure? The complaint about types.jl doesn't make sense to me.

lindahua added a commit that referenced this pull request Sep 20, 2013
Methods for working with distribution support added for univariate distributions
@lindahua lindahua merged commit 7727690 into JuliaStats:master Sep 20, 2013
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Thanks!

I will take care of those minor things.

@lindahua
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Support handling of all remaining univariate distributions is completed in commit 013643b.

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Great, thanks a lot for doing the rest

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Methods for divergence calculations
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