May 2022
Beginner to intermediate
448 pages
13h 7m
Korean
book
밑바닥부터 시작하는 데이터 과학 데이터 분석을 위한 파이썬 프로그래밍과 수학·통계 기초
by Joel Grus, Hangyeol Kim, Seongju Ha, Eunjung Park
Content preview from 밑바닥부터 시작하는 데이터 과학 데이터 분석을 위한 파이썬 프로그래밍과 수학·통계 기초
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from Scratch
107
z = a
_
b
_
test
_
statistic(1000, 200, 1000, 150) # -2.94
two
_
sided
_
p
_
value(z) # 0.003
즉, 두 광고가 동일하게 효과적이라면 이렇게 큰 차이가 발생할 확률은 불과
0
.
003
이라는 것을 의미한다.
7.7
베이즈 추론
지금까지 살펴본 방법들은 통계적 검정을 확률적으로 설명하는 것에(예를 들어
‘귀무가설이 사실이라면 이렇게 극단적인 통계치가 발생할 확률은
3
%이다’) 중
점을 두었다.
다른 추론 방법으로는 알려지지 않은 파라미터를 확률변수로 보는 방법이 있
다. 분석가(여러분)에게 파라미터에 대한
사전분포가 주어지고, 관측된 데이터
와 베이즈 정리를 사용하여
사후분포를 갱신할 수 있다. 통계적 검정에 대해 확
률적으로 결론을 내는 대신에 파라미터에 대해 확률적으로 결론을 낼 수 있다.
예를 들어 동전 던지기 예시처럼 알려지지 않은 파라미터가 확률이라고 해보자.
보통 모든 확률 값이
0
과
1
사이에서 정의되는 베타분포를 사전분포로 사용한다.
def B(alpha: float, beta: float) -> float:
"""
모든
확률값의
합이
1
이
되도록
해주는
정규화
값
"""
return math.gamma(alpha) * math.gamma(beta) / math.gamma(alpha ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.