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Simplify binomial approximation example with random.binomialvariate() (gh-113871)

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Raymond Hettinger 2024-01-09 13:02:07 -06:00 • committed by GitHub
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Doc/library/statistics.rst
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@ -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) >>> round(NormalDist(mu=n*p, sigma=sqrt(n*p*q)).cdf(k + 0.5), 4)
0.8402 0.8402
>>> # Solution using the cumulative binomial distribution >>> # Exact solution using the cumulative binomial distribution
>>> from math import comb, fsum >>> from math import comb, fsum
>>> round(fsum(comb(n, r) * p**r * q**(n-r) for r in range(k+1)), 4) >>> round(fsum(comb(n, r) * p**r * q**(n-r) for r in range(k+1)), 4)
0.8402 0.8402
>>> # Approximation using a simulation >>> # Approximation using a simulation
>>> from random import seed, choices >>> from random import seed, binomialvariate
>>> seed(8675309) >>> seed(8675309)
>>> def trial(): >>> mean(binomialvariate(n, p) <= k for i in range(10_000))
... return choices(('Python', 'Ruby'), (p, q), k=n).count('Python') 0.8406
...
>>> mean(trial() <= k for i in range(10_000))
0.8398
Naive bayesian classifier Naive bayesian classifier