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Finishes the translation oflibrary/statistics.po#474

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cschan1828 merged 4 commits intopython:3.11fromcschan1828:statistics
Jul 18, 2023
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35 changes: 32 additions & 3 deletionslibrary/statistics.po
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Original file line numberDiff line numberDiff line change
Expand Up@@ -9,7 +9,7 @@ msgstr ""
"Project-Id-Version: Python 3.11\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2023-05-03 00:17+0000\n"
"PO-Revision-Date: 2023-07-1400:44+0800\n"
"PO-Revision-Date: 2023-07-1417:53+0800\n"
"Last-Translator: Adrian Liaw <adrianliaw2000@gmail.com>\n"
"Language-Team: Chinese - TAIWAN (https://github.com/python/python-docs-zh-"
"tw)\n"
Expand DownExpand Up@@ -1224,6 +1224,7 @@ msgid ""
"Dividing a constant by an instance of :class:`NormalDist` is not supported "
"because the result wouldn't be normally distributed."
msgstr ""
"不支援將常數除以 :class:`NormalDist` 的實例，因為結果將不服從常態分布。"

#: ../../library/statistics.rst:877
msgid ""
Expand All@@ -1233,6 +1234,10 @@ msgid ""
"Sum_of_normally_distributed_random_variables>`_ represented as instances of :"
"class:`NormalDist`. For example:"
msgstr ""
"由於常態分布源自於獨立變數的加法效應 (additive effects)，因此可以\\ `將兩個獨"
"立的常態分布隨機變數相加與相減 <https://en.wikipedia.org/wiki/"
"Sum_of_normally_distributed_random_variables>`_，並且表示為 :class:"
"`NormalDist` 的實例。例如："

#: ../../library/statistics.rst:897
msgid ":class:`NormalDist` Examples and Recipes"
Expand All@@ -1250,6 +1255,9 @@ msgid ""
"determine the percentage of students with test scores between 1100 and 1200, "
"after rounding to the nearest whole number:"
msgstr ""
"例如，給定\\ `SAT 測驗的歷史資料 <https://nces.ed.gov/programs/digest/d17/"
"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"
"確定分數在 1100 與 1200 之間（四捨五入至最接近的整數）的學生的百分比。"

#: ../../library/statistics.rst:914
msgid ""
Expand All@@ -1265,13 +1273,19 @@ msgid ""
"analytically, :class:`NormalDist` can generate input samples for a `Monte "
"Carlo simulation <https://en.wikipedia.org/wiki/Monte_Carlo_method>`_:"
msgstr ""
"欲估計一個不易透過解析方法求解的模型的分布，:class:`NormalDist` 可以產生輸入"
"樣本以進行 `Monte Carlo 模擬 <https://en.wikipedia.org/wiki/"
"Monte_Carlo_method>`_："

#: ../../library/statistics.rst:940
msgid ""
"Normal distributions can be used to approximate `Binomial distributions "
"<https://mathworld.wolfram.com/BinomialDistribution.html>`_ when the sample "
"size is large and when the probability of a successful trial is near 50%."
msgstr ""
"當樣本數量夠大，且試驗成功的機率接近 50%，可以使用常態分布來近似 `二項分布 "
"(Binomial distributions) <https://mathworld.wolfram.com/BinomialDistribution."
"html>`_。"

#: ../../library/statistics.rst:945
msgid ""
Expand All@@ -1281,10 +1295,14 @@ msgid ""
"talks. Assuming the population preferences haven't changed, what is the "
"probability that the Python room will stay within its capacity limits?"
msgstr ""
"例如，一場有 750 位參加者的開源研討會中，有兩間可容納 500 人的會議室。一場是"
"關於 Python 的講座，另一場則是關於 Ruby 的。在過去的會議中，有 65% 的參加者傾"
"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"
"量限制的機率是？"

#: ../../library/statistics.rst:976
msgid "Normal distributions commonly arise in machine learning problems."
msgstr ""
msgstr "常態分布常在機器學習問題中出現。"

#: ../../library/statistics.rst:978
msgid ""
Expand All@@ -1293,19 +1311,24 @@ msgid ""
"challenge is to predict a person's gender from measurements of normally "
"distributed features including height, weight, and foot size."
msgstr ""
"維基百科有個 `Naive Bayesian Classifier 的優良範例 <https://en.wikipedia.org/"
"wiki/Naive_Bayes_classifier#Person_classification>`_。課題為從身高、體重與鞋"
"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。"

#: ../../library/statistics.rst:983
msgid ""
"We're given a training dataset with measurements for eight people. The "
"measurements are assumed to be normally distributed, so we summarize the "
"data with :class:`NormalDist`:"
msgstr ""
"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"
"利用 :class:`NormalDist` 來總結資料："

#: ../../library/statistics.rst:996
msgid ""
"Next, we encounter a new person whose feature measurements are known but "
"whose gender is unknown:"
msgstr ""
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："

#: ../../library/statistics.rst:1005
msgid ""
Expand All@@ -1314,10 +1337,16 @@ msgid ""
"the prior times the product of likelihoods for the feature measurements "
"given the gender:"
msgstr ""
"從可能為男性或女性的 50% `先驗機率 (prior probability) <https://en.wikipedia."
"org/wiki/Prior_probability>`_ 為開端，我們將後驗 (posterior) 機率計算為先驗機"
"率乘以給定性別下，各特徵量測值的概度乘積："

#: ../../library/statistics.rst:1020
msgid ""
"The final prediction goes to the largest posterior. This is known as the "
"`maximum a posteriori <https://en.wikipedia.org/wiki/"
"Maximum_a_posteriori_estimation>`_ or MAP:"
msgstr ""
"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 "
"posteriori) <https://en.wikipedia.org/wiki/"
"Maximum_a_posteriori_estimation>`_ 或者 MAP："
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