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https://issues.apache.org/jira/browse/RNG-146?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17369009#comment-17369009
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Gilles Sadowski commented on RNG-146:
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bq. Should a similar trivial limit be set for a scaled Gaussian sampler?
It won't hurt.
Do use-cases exist for such large standard deviations?
Won't issues still exist for a mean and standard deviation of very different
magnitudes?
> GaussianSampler should not allow infinite standard deviation
> ------------------------------------------------------------
>
> Key: RNG-146
> URL: https://issues.apache.org/jira/browse/RNG-146
> Project: Commons RNG
> Issue Type: Bug
> Components: sampling
> Affects Versions: 1.3
> Reporter: Alex Herbert
> Priority: Trivial
>
> The GaussianSampler requires the standard deviation is strictly positive. It
> allows an infinite value. This will produce a NaN output if the
> NormalizedGaussianSampler returns 0:
> {code:java}
> @Test
> public void testInfiniteStdDev() {
> NormalizedGaussianSampler gauss = new NormalizedGaussianSampler() {
> @Override
> public double sample() {
> return 0;
> }
> };
> GaussianSampler s = new GaussianSampler(gauss, 0,
> Double.POSITIVE_INFINITY);
> Assert.assertEquals(Double.NaN, s.sample(), 0.0);
> }
> {code}
> A fix is to require the standard deviation is finite.
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