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author | Matt Stephanson <stephanson.matt@gmail.com> | 2024-05-04 04:59:38 -0700 |
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committer | GitHub <noreply@github.com> | 2024-05-04 13:59:38 +0200 |
commit | 76aa042dde6ba9ba57c680950f5818259ee02690 (patch) | |
tree | 77f117e6379450198b1291a08ea802a546f838e6 /llvm/lib/Support/VirtualFileSystem.cpp | |
parent | caacf8685ac49526103b748b6b439dea84c30274 (diff) | |
download | llvm-76aa042dde6ba9ba57c680950f5818259ee02690.zip llvm-76aa042dde6ba9ba57c680950f5818259ee02690.tar.gz llvm-76aa042dde6ba9ba57c680950f5818259ee02690.tar.bz2 |
[libc++] Adjust some of the [rand.dist] critical values that are too strict (#88669)
Adjust some of the [rand.dist] critical values that are too strict
- Most critical values are determined empirically by running each test
51
times with a different PRNG seed and finding the smallest symmetric
interval
around the median that contains 90% of the sample means, variances, etc.
- For the Kolmogorov-Smirnov tests, the alpha=0.1 critical value for
large N
is 1.224/sqrt(N).
- For normally distributed variates, the sample kurtosis is distributed
as
Normal(0, 24/N). For N=1e5, this gives a 90% confidence interval of
0+/-0.0255. For Binomial(40, 0.25), which is approximately normal, the
kurtosis is -0.0167, so the relative 90% CI is large, on the order of
0.0255/0.0167 = 153%. In most cases the distribution of the sample
kurtosis
isn't known analytically, but similarly large relative tolerances can be
expected if the kurtosis is near zero.
Diffstat (limited to 'llvm/lib/Support/VirtualFileSystem.cpp')
0 files changed, 0 insertions, 0 deletions