@@ -16,7 +16,7 @@ msgid ""
1616msgstr ""
1717"Project-Id-Version : Python 3.8\n "
1818"Report-Msgid-Bugs-To : \n "
19- "POT-Creation-Date : 2020-01-01 12:19 +0000\n "
19+ "POT-Creation-Date : 2020-01-26 12:31 +0000\n "
2020"PO-Revision-Date : 2017-02-16 23:27+0000\n "
2121"
Last-Translator :
Freesand Leo <[email protected] >, 2019\n "
2222"Language-Team : Chinese (China) (https://www.transifex.com/python-doc/teams/5390/zh_CN/)\n "
@@ -1001,10 +1001,26 @@ msgstr ""
10011001"<https://en.wikipedia.org/wiki/Monte_Carlo_method>`_ 的输入样本:"
10021002
10031003#: /home/travis/build/python/cpython-doc-catalog/Doc/library/statistics.rst:775
1004+ msgid ""
1005+ "Normal distributions can be used to approximate `Binomial distributions "
1006+ "<http://mathworld.wolfram.com/BinomialDistribution.html>`_ when the sample "
1007+ "size is large and when the probability of a successful trial is near 50%."
1008+ msgstr ""
1009+
1010+ #: /home/travis/build/python/cpython-doc-catalog/Doc/library/statistics.rst:780
1011+ msgid ""
1012+ "For example, an open source conference has 750 attendees and two rooms with "
1013+ "a 500 person capacity. There is a talk about Python and another about Ruby."
1014+ " In previous conferences, 65% of the attendees preferred to listen to Python"
1015+ " talks. Assuming the population preferences haven't changed, what is the "
1016+ "probability that the rooms will stay within their capacity limits?"
1017+ msgstr ""
1018+
1019+ #: /home/travis/build/python/cpython-doc-catalog/Doc/library/statistics.rst:811
10041020msgid "Normal distributions commonly arise in machine learning problems."
10051021msgstr "在机器学习问题中也经常会出现正态分布。"
10061022
1007- #: /home/travis/build/python/cpython-doc-catalog/Doc/library/statistics.rst:777
1023+ #: /home/travis/build/python/cpython-doc-catalog/Doc/library/statistics.rst:813
10081024msgid ""
10091025"Wikipedia has a `nice example of a Naive Bayesian Classifier "
10101026"<https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Sex_classification>`_."
@@ -1015,20 +1031,20 @@ msgstr ""
10151031"<https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Sex_classification>`_。"
10161032" 挑战的问题是根据对多个正态分布的特征测量值包括身高、体重和足部尺码来预测一个人的性别。"
10171033
1018- #: /home/travis/build/python/cpython-doc-catalog/Doc/library/statistics.rst:782
1034+ #: /home/travis/build/python/cpython-doc-catalog/Doc/library/statistics.rst:818
10191035msgid ""
10201036"We're given a training dataset with measurements for eight people. The "
10211037"measurements are assumed to be normally distributed, so we summarize the "
10221038"data with :class:`NormalDist`:"
10231039msgstr "我们得到了由八个人的测量值组成的训练数据集。 假定这些测量值是正态分布的,因此我们用 :class:`NormalDist` 来总结数据:"
10241040
1025- #: /home/travis/build/python/cpython-doc-catalog/Doc/library/statistics.rst:795
1041+ #: /home/travis/build/python/cpython-doc-catalog/Doc/library/statistics.rst:831
10261042msgid ""
10271043"Next, we encounter a new person whose feature measurements are known but "
10281044"whose gender is unknown:"
10291045msgstr "接下来,我们遇到一个特征测量值已知但性别未知的新人:"
10301046
1031- #: /home/travis/build/python/cpython-doc-catalog/Doc/library/statistics.rst:804
1047+ #: /home/travis/build/python/cpython-doc-catalog/Doc/library/statistics.rst:840
10321048msgid ""
10331049"Starting with a 50% `prior probability "
10341050"<https://en.wikipedia.org/wiki/Prior_probability>`_ of being male or female,"
@@ -1038,7 +1054,7 @@ msgstr ""
10381054"从是男是女各 50% 的 `先验概率 <https://en.wikipedia.org/wiki/Prior_probability>`_ "
10391055"出发,我们通过将该先验概率乘以给定性别的特征度量值的可能性累积值来计算后验概率:"
10401056
1041- #: /home/travis/build/python/cpython-doc-catalog/Doc/library/statistics.rst:819
1057+ #: /home/travis/build/python/cpython-doc-catalog/Doc/library/statistics.rst:855
10421058msgid ""
10431059"The final prediction goes to the largest posterior. This is known as the "
10441060"`maximum a posteriori "
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