8000 Add statistics recipe for sampling from an estimated probability density distribution by rhettinger · Pull Request #117221 · python/cpython · GitHub
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Add statistics recipe for sampling from an estimated probability density distribution #117221

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Merged
merged 16 commits into from
Mar 27, 2024
Merged

Add statistics recipe for sampling from an estimated probability density distribution #117221

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72a2d74
kde_random() recipe
rhettinger Mar 25, 2024
4117a61
Tweak markup and wording
rhettinger Mar 25, 2024
4bd19c9
The aliases are visual clutter and not essential to the recipe.
rhettinger Mar 26, 2024
211836c
Add invcdf() / cdf() round trip tests
rhettinger Mar 26, 2024
6f0aafc
.
rhettinger Mar 26, 2024
e2c8d1c
Improve docstring
rhettinger Mar 26, 2024
4fbfdfa
Merge branch 'main' into kde_rand_recipe
rhettinger Mar 26, 2024
7ac2b83
More direct description of the recipe
rhettinger Mar 26, 2024
4398863
Tighter wording
rhettinger Mar 26, 2024
e827d8a
Separate core logic from formatting in the example
rhettinger Mar 26, 2024
d30e554
Moving seeding to just before calling rand()
rhettinger Mar 26, 2024
2a570d9
Remove spurious closing paren
rhettinger Mar 27, 2024
d17e698
More brief section title
rhettinger Mar 27, 2024
a56b108
choice() and random() do not commute
rhettinger Mar 27, 2024
02ed395
Fix typo
rhettinger Mar 27, 2024
d3e0e40
Merge branch 'main' into kde_rand_recipe
rhettinger Mar 27, 2024
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Improve docstring
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@rhettinger
rhettinger authored Mar 26, 2024
commit e2c8d1c92affcc74d0188e2226bd73c7c6834757
4 changes: 2 additions & 2 deletions Doc/library/statistics.rst
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Expand Up @@ -1154,7 +1154,7 @@ The :func:`kde()` function creates a continuous probability density
function from discrete samples. Some applications need a way to make
random selections from that distribution.

Since the estimated function is just the sum of kernels centered at each
Since the smoothed probability density function is just the sum of kernels centered at each
data point, the problem can be reduced to sampling the kernel function
and recentering the result around a randomly chosen data point. This
works for kernels that have known or accurately approximated inverse
Expand All @@ -1176,7 +1176,7 @@ cumulative distribution functions.
}

def kde_random(data, h, kernel='normal'):
'Generate random a sample from an estimated kernel density function.'
'Generate a random sample from a smoothed probability density function.'
kernel_invcdf = kernel_invcdfs[kernel]
def rand():
return choice(data) + h * kernel_invcdf(random())
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