feat(library/statistics): translate newly-added strings (python#627)

Author: mattwang44

library/statistics.po

@@ -1156,7 +1156,7 @@ msgid ""
 "random variable *X* will be near the given value *x*. Mathematically, it is "
 "the limit of the ratio ``P(x <= X < x+dx) / dx`` as *dx* approaches zero."
 msgstr ""
-"利用\\ `機率密度函式 (probability density function, pdf) <https://en."
+"利用\\ `機率密度函數 (probability density function, pdf) <https://en."
 "wikipedia.org/wiki/Probability_density_function>`_ 計算隨機變數 *X* 接近給定"
 "值 *x* 的相對概度 (relative likelihood)。數學上，它是比率 ``P(x <= X < "
 "x+dx) / dx`` 在 *dx* 趨近於零時的極限值。"
@@ -1277,7 +1277,7 @@ msgstr ":class:`NormalDist` 範例與錦囊妙計"
 
 #: ../../library/statistics.rst:927
 msgid "Classic probability problems"
-msgstr ""
+msgstr "經典機率問題"
 
 #: ../../library/statistics.rst:929
 msgid ":class:`NormalDist` readily solves classic probability problems."
@@ -1305,7 +1305,7 @@ msgstr ""
 
 #: ../../library/statistics.rst:956
 msgid "Monte Carlo inputs for simulations"
-msgstr ""
+msgstr "用於模擬的蒙地卡羅 (Monte Carlo) 輸入"
 
 #: ../../library/statistics.rst:958
 msgid ""
@@ -1314,12 +1314,12 @@ msgid ""
 "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/"
+"樣本以進行\\ `蒙地卡羅模擬 <https://en.wikipedia.org/wiki/"
 "Monte_Carlo_method>`_："
 
 #: ../../library/statistics.rst:975
 msgid "Approximating binomial distributions"
-msgstr ""
+msgstr "近似二項分布"
 
 #: ../../library/statistics.rst:977
 msgid ""
@@ -1346,7 +1346,7 @@ msgstr ""
 
 #: ../../library/statistics.rst:1016
 msgid "Naive bayesian classifier"
-msgstr ""
+msgstr "單純貝氏分類器 (Naive bayesian classifier)"
 
 #: ../../library/statistics.rst:1018
 msgid "Normal distributions commonly arise in machine learning problems."
@@ -1401,13 +1401,13 @@ msgstr ""
 
 #: ../../library/statistics.rst:1073
 msgid "Kernel density estimation"
-msgstr ""
+msgstr "核密度估計 (Kernel density estimation)"
 
 #: ../../library/statistics.rst:1075
 msgid ""
 "It is possible to estimate a continuous probability density function from a "
 "fixed number of discrete samples."
-msgstr ""
+msgstr "可以從固定數量的離散樣本估計出連續機率密度函式。"
 
 #: ../../library/statistics.rst:1078
 msgid ""
@@ -1418,6 +1418,9 @@ msgid ""
 "smoothing is controlled by a single parameter, ``h``, representing the "
 "variance of the kernel function."
 msgstr ""
+"基本想法是使用\\ `一個核函式如常態分布、三角分布或均勻分布 <https://en."
+"wikipedia.org/wiki/Kernel_(statistics)#Kernel_functions_in_common_use>`_\\ 來"
+"使資料更加平滑。平滑程度由單個參數 ``h`` 控制，代表核函數的變異數。"
 
 #: ../../library/statistics.rst:1097
 msgid ""
@@ -1426,11 +1429,14 @@ msgid ""
 "recipe to generate and plot a probability density function estimated from a "
 "small sample:"
 msgstr ""
+"`維基百科有一個範例 <https://en.wikipedia.org/wiki/"
+"Kernel_density_estimation#Example>`_，我們可以使用 ``kde_normal()`` 這個錦囊"
+"妙計來生成並繪製從小樣本估計的機率密度函式："
 
 #: ../../library/statistics.rst:1109
 msgid "The points in ``xarr`` and ``yarr`` can be used to make a PDF plot:"
-msgstr ""
+msgstr "``xarr`` 和 ``yarr`` 中的點可用於繪製 PDF 圖："
 
 #: ../../library/statistics.rst:-1
 msgid "Scatter plot of the estimated probability density function."
-msgstr ""
+msgstr "估計機率密度函式的散點圖 (scatter plot)。"