[
https://issues.apache.org/jira/browse/MATH-1384?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Arman Bilge updated MATH-1384:
------------------------------
Description:
For certain edge cases, HypergeometricDistribution.logProbability() will return
NaN.
To compute the hypergeometric log probability, three binomial log probabilities
are computed and then combined accordingly. The implementation is essentially
the same as in BinomialDistribution.logProbability() and uses the
SaddlePointExpansion. However, the Binomial implementation includes an extra
check for the edge case of 0 trials which the HyperGeometric lacks.
An example call which fails is:
new HypergeometricDistribution(null, 11, 0, 1).logProbability(0)
which returns NaN instead of 0.0.
Note that
new HypergeometricDistribution(null, 10, 0, 1).logProbability(0)
returns 0 as expected.
Possible fixes:
1. Check for the edge cases and return appropriate values. This would make the
code somewhat more complex.
2. Instead of duplicating the implementation use
BinomialDistribution.logProbability(). This is much simpler/more readable but
will reduce performance as each call to BinomialDistribution.logProbability()
makes redundant checks of validity of input parameters etc.
I am happy to submit a PR at the GitHub repo implementing either 1 or 2 with
the necessary tests.
was:
For certain edge cases, HypergeometricDistribution.logProbability() will return
NaN.
To compute the hypergeometric log probability, three binomial log probabilities
are computed and then combined accordingly. The implementation is essentially
the same as in BinomialDistribution.logProbability() and uses the
SaddlePointExpansion. However, the Binomial implementation includes an extra
check for the edge case of 0 trials which the HyperGeometric lacks.
An example call which fails is:
new HypergeometricDistribution(null, 11, 0, 1).logProbability(0)
which returns NaN instead of 0.0.
Note that
`new HypergeometricDistribution(null, 10, 0, 1).logProbability(0)`
returns 0 as expected.
Possible fixes:
1. Check for the edge cases and return appropriate values. This would make the
code somewhat more complex.
2. Instead of duplicating the implementation use
BinomialDistribution.logProbability(). This is much simpler/more readable but
will reduce performance as each call to BinomialDistribution.logProbability()
makes redundant checks of validity of input parameters etc.
I am happy to submit a PR at the GitHub repo implementing either 1 or 2 with
the necessary tests.
> HypergeometricDistribution logProbability() returns NaN for edge cases
> ----------------------------------------------------------------------
>
> Key: MATH-1384
> URL: https://issues.apache.org/jira/browse/MATH-1384
> Project: Commons Math
> Issue Type: Bug
> Affects Versions: 3.0, 4.0
> Reporter: Arman Bilge
> Priority: Minor
>
> For certain edge cases, HypergeometricDistribution.logProbability() will
> return NaN.
> To compute the hypergeometric log probability, three binomial log
> probabilities are computed and then combined accordingly. The implementation
> is essentially the same as in BinomialDistribution.logProbability() and uses
> the SaddlePointExpansion. However, the Binomial implementation includes an
> extra check for the edge case of 0 trials which the HyperGeometric lacks.
> An example call which fails is:
> new HypergeometricDistribution(null, 11, 0, 1).logProbability(0)
> which returns NaN instead of 0.0.
> Note that
> new HypergeometricDistribution(null, 10, 0, 1).logProbability(0)
> returns 0 as expected.
> Possible fixes:
> 1. Check for the edge cases and return appropriate values. This would make
> the code somewhat more complex.
> 2. Instead of duplicating the implementation use
> BinomialDistribution.logProbability(). This is much simpler/more readable but
> will reduce performance as each call to BinomialDistribution.logProbability()
> makes redundant checks of validity of input parameters etc.
> I am happy to submit a PR at the GitHub repo implementing either 1 or 2 with
> the necessary tests.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
