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Data Science from Scratch中文版|用Python學資料科學
book

Data Science from Scratch中文版|用Python學資料科學

by Joel Grus
October 2016
Beginner to intermediate
328 pages
6h 58m
Chinese
GoTop Information, Inc.
Content preview from Data Science from Scratch中文版|用Python學資料科學
69
第六章
機率
機率理論一般來說都是對的,但在某些情況下卻很荒謬。
——愛德華 · 吉朋(Edward Gibbon
如果對
機率
和相關數學沒有一定程度的瞭解,恐怕很難做好資料科學的工作。不過本
章的做法和第 5 章很類似,我們只會做些簡要的介紹,而不會深入太多的細節。
我們的目的,就是希望能讓你理解機率的概念,它指的是從某個事件
空間
universe
of
events)中挑選出某個
事件
event
),衡量其中不確定性的一種量化方式。如果想要搞
懂前面這種充滿術語的說法,還不如直接來個例子:各位可以想想丟骰子的情況。所
謂的事件空間,其中包含了所有可能的結果。若從其中取出部分結果,就可以形成一
個事件;舉例來說,「骰子丟出 1 點」和「骰子丟出偶數」都可算是一個事件。
我們會用
P
(
E
) 這樣的寫法,來表示「事件
E
的機率」。
我們可以用機率理論來建立模型,也可以用機率理論來評估模型。事實上,我們到處
都會不斷用到機率理論。
如果你有興趣的話,可以進一步深入理解機率的哲學與
內涵
(如果還能來瓶啤酒,那
就更棒了!)。不過,我們在這裡並不會做太深入的說明。
相依與獨立
大體上來說,如果我們知道「事件
E
是否發生」可以協助我們判斷「事件
F
是否發
生」(或是反過來的情況),我們就會說
E
F
這兩個事件是「
相依的
」(
dependent
)。
要不然的話,我們就會說這兩個事件是「
獨立的
」(
independent
)。
舉例來說,假設我們丟兩次銅板,即使知道「第一次丟出正面」,我們還是無法知道
「第二次丟出正面」的情況會不會發生。這
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Publisher Resources

ISBN: 9789864761982