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| author | Raymond Hettinger <rhettinger@users.noreply.github.com> | 2024-01-09 19:02:07 (GMT) |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2024-01-09 19:02:07 (GMT) |
| commit | 2fd2e747930987eb8ed4929cf0132e85db759dab (patch) | |
| tree | a8e62f4ffcc1425d68c2f984e480b532ddf9a874 | |
| parent | 65f8eb71190f870c66fb00da29a670ee232a3fd5 (diff) | |
| download | cpython-2fd2e747930987eb8ed4929cf0132e85db759dab.zip cpython-2fd2e747930987eb8ed4929cf0132e85db759dab.tar.gz cpython-2fd2e747930987eb8ed4929cf0132e85db759dab.tar.bz2 | |
Simplify binomial approximation example with random.binomialvariate() (gh-113871)
| -rw-r--r-- | Doc/library/statistics.rst | 11 |
1 files changed, 4 insertions, 7 deletions
diff --git a/Doc/library/statistics.rst b/Doc/library/statistics.rst index 5c8ad3a..588c9c0 100644 --- a/Doc/library/statistics.rst +++ b/Doc/library/statistics.rst @@ -1026,19 +1026,16 @@ probability that the Python room will stay within its capacity limits? >>> round(NormalDist(mu=n*p, sigma=sqrt(n*p*q)).cdf(k + 0.5), 4) 0.8402 - >>> # Solution using the cumulative binomial distribution + >>> # Exact solution using the cumulative binomial distribution >>> from math import comb, fsum >>> round(fsum(comb(n, r) * p**r * q**(n-r) for r in range(k+1)), 4) 0.8402 >>> # Approximation using a simulation - >>> from random import seed, choices + >>> from random import seed, binomialvariate >>> seed(8675309) - >>> def trial(): - ... return choices(('Python', 'Ruby'), (p, q), k=n).count('Python') - ... - >>> mean(trial() <= k for i in range(10_000)) - 0.8398 + >>> mean(binomialvariate(n, p) <= k for i in range(10_000)) + 0.8406 Naive bayesian classifier |