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Commit758b578

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sync with cpython e8963a86
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‎library/statistics.po

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@@ -8,7 +8,7 @@ msgid ""
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msgstr ""
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"Project-Id-Version:Python 3.12\n"
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"Report-Msgid-Bugs-To:\n"
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"POT-Creation-Date:2023-05-03 00:17+0000\n"
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"POT-Creation-Date:2023-08-15 00:03+0000\n"
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"PO-Revision-Date:2023-07-22 21:15+0800\n"
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"Last-Translator:Adrian Liaw <adrianliaw2000@gmail.com>\n"
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"Language-Team:Chinese - TAIWAN (https://github.com/python/python-docs-zh-"
@@ -1275,11 +1275,15 @@ msgstr ""
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msgid":class:`NormalDist` Examples and Recipes"
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msgstr":class:`NormalDist` 範例與錦囊妙計"
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#:../../library/statistics.rst:925
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#:../../library/statistics.rst:927
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msgid"Classic probability problems"
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msgstr""
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#:../../library/statistics.rst:929
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msgid":class:`NormalDist` readily solves classic probability problems."
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msgstr":class:`NormalDist` 可以輕易地解決經典的機率問題。"
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#:../../library/statistics.rst:927
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#:../../library/statistics.rst:931
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msgid""
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"For example, given `historical data for SAT exams <https://nces.ed.gov/"
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"programs/digest/d17/tables/dt17_226.40.asp>`_ showing that scores are "
@@ -1291,15 +1295,19 @@ msgstr ""
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"tables/dt17_226.40.asp>`_，顯示成績為平均 1060、標準差 195 的常態分布。我們要"
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"求出分數在 1100 與 1200 之間（四捨五入至最接近的整數）的學生的百分比："
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#:../../library/statistics.rst:940
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#:../../library/statistics.rst:944
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msgid""
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"Find the `quartiles <https://en.wikipedia.org/wiki/Quartile>`_ and `deciles "
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"<https://en.wikipedia.org/wiki/Decile>`_ for the SAT scores:"
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msgstr""
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"找出 SAT 分數的\\ `四分位數 <https://en.wikipedia.org/wiki/Quartile>`_\\ 以及"
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"\\ `十分位數 <https://en.wikipedia.org/wiki/Decile>`_："
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#:../../library/statistics.rst:950
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#:../../library/statistics.rst:956
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msgid"Monte Carlo inputs for simulations"
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msgstr""
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#:../../library/statistics.rst:958
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msgid""
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"To estimate the distribution for a model than isn't easy to solve "
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"analytically, :class:`NormalDist` can generate input samples for a `Monte "
@@ -1309,7 +1317,11 @@ msgstr ""
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"樣本以進行 `Monte Carlo 模擬 <https://en.wikipedia.org/wiki/"
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"Monte_Carlo_method>`_："
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#:../../library/statistics.rst:966
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#:../../library/statistics.rst:975
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msgid"Approximating binomial distributions"
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msgstr""
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#:../../library/statistics.rst:977
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msgid""
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"Normal distributions can be used to approximate `Binomial distributions "
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"<https://mathworld.wolfram.com/BinomialDistribution.html>`_ when the sample "
@@ -1319,7 +1331,7 @@ msgstr ""
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"(Binomial distributions) <https://mathworld.wolfram.com/BinomialDistribution."
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"html>`_。"
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#:../../library/statistics.rst:971
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#:../../library/statistics.rst:982
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msgid""
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"For example, an open source conference has 750 attendees and two rooms with "
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"a 500 person capacity. There is a talk about Python and another about Ruby. "
@@ -1332,11 +1344,15 @@ msgstr ""
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"向參與 Python 講座。假設參與者的偏好沒有改變，那麼 Python 會議室未超過自身容"
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"量限制的機率是？"
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#:../../library/statistics.rst:1003
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#:../../library/statistics.rst:1016
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msgid"Naive bayesian classifier"
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msgstr""
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#:../../library/statistics.rst:1018
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msgid"Normal distributions commonly arise in machine learning problems."
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msgstr"常態分布常在機器學習問題中出現。"
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#:../../library/statistics.rst:1005
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#:../../library/statistics.rst:1020
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msgid""
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"Wikipedia has a `nice example of a Naive Bayesian Classifier <https://en."
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"wikipedia.org/wiki/Naive_Bayes_classifier#Person_classification>`_. The "
@@ -1347,7 +1363,7 @@ msgstr ""
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"wiki/Naive_Bayes_classifier#Person_classification>`_。課題為從身高、體重與鞋"
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"子尺寸等符合常態分布的特徵量測值中判斷一個人的性別。"
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#:../../library/statistics.rst:1010
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#:../../library/statistics.rst:1025
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msgid""
13521368
"We're given a training dataset with measurements for eight people. The "
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"measurements are assumed to be normally distributed, so we summarize the "
@@ -1356,13 +1372,13 @@ msgstr ""
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"給定一組包含八個人的量測值的訓練資料集。假設這些量測值服從常態分布，我們可以"
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"利用 :class:`NormalDist` 來總結資料："
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#:../../library/statistics.rst:1023
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#:../../library/statistics.rst:1038
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msgid""
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"Next, we encounter a new person whose feature measurements are known but "
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"whose gender is unknown:"
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msgstr"接著，我們遇到一個新的人，他的特徵量測值已知，但性別未知："
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#:../../library/statistics.rst:1032
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#:../../library/statistics.rst:1047
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msgid""
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"Starting with a 50% `prior probability <https://en.wikipedia.org/wiki/"
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"Prior_probability>`_ of being male or female, we compute the posterior as "
@@ -1373,7 +1389,7 @@ msgstr ""
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"org/wiki/Prior_probability>`_ 為開端，我們將後驗機率 (posterior probability) "
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"計算為先驗機率乘以給定性別下，各特徵量測值的概度乘積："
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#:../../library/statistics.rst:1047
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#:../../library/statistics.rst:1062
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msgid""
13781394
"The final prediction goes to the largest posterior. This is known as the "
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"`maximum a posteriori <https://en.wikipedia.org/wiki/"
@@ -1382,3 +1398,39 @@ msgstr ""
13821398
"最終的預測結果將取決於最大的後驗機率。這被稱為\\ `最大後驗機率 (maximum a "
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"posteriori) <https://en.wikipedia.org/wiki/"
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"Maximum_a_posteriori_estimation>`_ 或者 MAP："
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#:../../library/statistics.rst:1073
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msgid"Kernel density estimation"
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msgstr""
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1406+
#:../../library/statistics.rst:1075
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msgid""
1408+
"It is possible to estimate a continuous probability density function from a "
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"fixed number of discrete samples."
1410+
msgstr""
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#:../../library/statistics.rst:1078
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msgid""
1414+
"The basic idea is to smooth the data using `a kernel function such as a "
1415+
"normal distribution, triangular distribution, or uniform distribution "
1416+
"<https://en.wikipedia.org/wiki/"
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"Kernel_(statistics)#Kernel_functions_in_common_use>`_. The degree of "
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"smoothing is controlled by a single parameter, ``h``, representing the "
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"variance of the kernel function."
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msgstr""
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#:../../library/statistics.rst:1097
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msgid""
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"`Wikipedia has an example <https://en.wikipedia.org/wiki/"
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"Kernel_density_estimation#Example>`_ where we can use the ``kde_normal()`` "
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"recipe to generate and plot a probability density function estimated from a "
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"small sample:"
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msgstr""
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#:../../library/statistics.rst:1109
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msgid"The points in ``xarr`` and ``yarr`` can be used to make a PDF plot:"
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msgstr""
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#:../../library/statistics.rst:-1
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msgid"Scatter plot of the estimated probability density function."
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msgstr""

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