Phil Steitz wrote:
J.Pietschmann wrote:

Phil Steitz wrote:

1) Decide what to do about inverse cumulative probabilities where p = 1 (easy solution is to document and throw)



Nearly +1


My own "nearly +1" on this just turned to -1. After looking some more at the code and thinking some more, I think that both p=1 and p=0 should be handled correctly in all cases. The difficult cases are when the probability density function has unbounded support. Here is what I propose for the values of inverseCumulativeProbability() at p=0 and p=1 for current distributions. Unless otherwise noted, these values are intented to be independent of distribution parameters.


Distribution         p=0                     p=1
------------------------------------------------------------------
Binomial               0               Integer.MAX_VALUE
Chisquare              0               Double.POSITIVE_INFINITY
Exponential            0               Double.POSITIVE_INFINITY
F                      0               Double.POSITIVE_INFINITY
Gamma                  0               Double.POSITIVE_INFINITY
HyperGeometric         0               finite, parameter-dependent
Normal       Double.NEGATIVE_INFINITY  Double.POSITIVE_INFINITY
T            Double.NEGATIVE_INFINITY  Double.POSITIVE_INFINITY

Other than the value for Chisquare with p=1 (which causes R to hang), these values are consistent with what R returns using the q* functions. It might be more convenient to return Double.MAX_VALUE, -Double.MAX_VALUE in place of the INFINITY's (since then we could just use getDomainLowerBound at 0 and 1) but this would not be correct mathematically. If there are no objections, I will find a way to get the values above returned.

I have committed changes and tests to ensure that the values in the table above are returned, modulo correcting the following mistakes:


Both of the discrete distributions (Binomial and Hypergeometric) should return -1 for the inverseCumulativeProbability(0). The definition that we are using is that inverseCumulativeProbability(p) = the largest x such that
P(X <= x) <= p.


Since 0 has positive probability for both the Binomial and Hypergeometric distributions, and the function is integer-valued, the correct value to return in these cases is actually -1, not 0.

Phil






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