@@ -1156,7 +1156,7 @@ msgid ""
11561156"random variable *X* will be near the given value *x*. Mathematically, it is "
11571157"the limit of the ratio ``P(x <= X < x+dx) / dx`` as *dx* approaches zero."
11581158msgstr ""
1159- "利用\\ `機率密度函式 (probability density function, pdf) <https://en."
1159+ "利用\\ `機率密度函數 (probability density function, pdf) <https://en."
11601160"wikipedia.org/wiki/Probability_density_function>`_ 計算隨機變數 *X* 接近給定"
11611161"值 *x* 的相對概度 (relative likelihood)。數學上，它是比率 ``P(x <= X < "
11621162"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"
12771277
12781278#: ../../library/statistics.rst:927
12791279msgid "Classic probability problems"
1280- msgstr ""
1280+ msgstr "經典機率問題 "
12811281
12821282#: ../../library/statistics.rst:929
12831283msgid ":class:`NormalDist` readily solves classic probability problems."
@@ -1305,7 +1305,7 @@ msgstr ""
13051305
13061306#: ../../library/statistics.rst:956
13071307msgid "Monte Carlo inputs for simulations"
1308- msgstr ""
1308+ msgstr "用於模擬的蒙地卡羅 (Monte Carlo) 輸入 "
13091309
13101310#: ../../library/statistics.rst:958
13111311msgid ""
@@ -1314,12 +1314,12 @@ msgid ""
13141314"Carlo simulation <https://en.wikipedia.org/wiki/Monte_Carlo_method>`_:"
13151315msgstr ""
13161316"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"
1317- "樣本以進行 `Monte Carlo 模擬 <https://en.wikipedia.org/wiki/"
1317+ "樣本以進行\\ `蒙地卡羅模擬 <https://en.wikipedia.org/wiki/"
13181318"Monte_Carlo_method>`_："
13191319
13201320#: ../../library/statistics.rst:975
13211321msgid "Approximating binomial distributions"
1322- msgstr ""
1322+ msgstr "近似二項分布 "
13231323
13241324#: ../../library/statistics.rst:977
13251325msgid ""
@@ -1346,7 +1346,7 @@ msgstr ""
13461346
13471347#: ../../library/statistics.rst:1016
13481348msgid "Naive bayesian classifier"
1349- msgstr ""
1349+ msgstr "單純貝氏分類器 (Naive bayesian classifier) "
13501350
13511351#: ../../library/statistics.rst:1018
13521352msgid "Normal distributions commonly arise in machine learning problems."
@@ -1401,13 +1401,13 @@ msgstr ""
14011401
14021402#: ../../library/statistics.rst:1073
14031403msgid "Kernel density estimation"
1404- msgstr ""
1404+ msgstr "核密度估計 (Kernel density estimation) "
14051405
14061406#: ../../library/statistics.rst:1075
14071407msgid ""
14081408"It is possible to estimate a continuous probability density function from a "
14091409"fixed number of discrete samples."
1410- msgstr ""
1410+ msgstr "可以從固定數量的離散樣本估計出連續機率密度函式。 "
14111411
14121412#: ../../library/statistics.rst:1078
14131413msgid ""
@@ -1418,6 +1418,9 @@ msgid ""
14181418"smoothing is controlled by a single parameter, ``h``, representing the "
14191419"variance of the kernel function."
14201420msgstr ""
1421+ "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."
1422+ "wikipedia.org/wiki/Kernel_(statistics)#Kernel_functions_in_common_use>`_\\ 來"
1423+ "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。"
14211424
14221425#: ../../library/statistics.rst:1097
14231426msgid ""
@@ -1426,11 +1429,14 @@ msgid ""
14261429"recipe to generate and plot a probability density function estimated from a "
14271430"small sample:"
14281431msgstr ""
1432+ "`維基百科有一個範例 <https://en.wikipedia.org/wiki/"
1433+ "Kernel_density_estimation#Example>`_，我們可以使用 ``kde_normal()`` 這個錦囊"
1434+ "妙計來生成並繪製從小樣本估計的機率密度函式："
14291435
14301436#: ../../library/statistics.rst:1109
14311437msgid "The points in ``xarr`` and ``yarr`` can be used to make a PDF plot:"
1432- msgstr ""
1438+ msgstr "``xarr`` 和 ``yarr`` 中的點可用於繪製 PDF 圖： "
14331439
14341440#: ../../library/statistics.rst: -1
14351441msgid "Scatter plot of the estimated probability density function."
1436- msgstr ""
1442+ msgstr "估計機率密度函式的散點圖 (scatter plot)。 "