評估機率分配的選擇

Python 的 Monte Carlo 模擬

Izzy Weber

Curriculum Manager, DataCamp

選擇變數的機率分配

建立對資料與可用機率分配的直觀理解
用最大概似估計（MLE）比較候選分配
用 Kolmogorov–Smirnov 檢定評估機率分配的「配適度」
- 量化資料的經驗分配與理論候選機率分配之間的距離
- 使用 scipy.stats.kstest() 計算

評估分配選擇：年齡

results = []

list_of_dists = ["laplace", "norm", "expon"]

for i in list_of_dists:
    dist = getattr(st, i)

    param = dist.fit(dia["age"])

    result = st.kstest(dia["age"], i, args=param)

    print(result)

依序為 Laplace、常態與指數分配的結果：

KstestResult(statistic=0.09511179937112832, pvalue=0.0006239579389182981)
KstestResult(statistic=0.0615913626181368, pvalue=0.06703225234359811)
KstestResult(statistic=0.2536037941921312, pvalue=1.5202547969084796e-25)

評估分配選擇：年齡

依序為 Laplace、常態與指數分配的結果：

KstestResult(statistic=0.09511179937112832, pvalue=0.0006239579389182981)
KstestResult(statistic=0.0615913626181368, pvalue=0.06703225234359811)
KstestResult(statistic=0.2536037941921312, pvalue=1.5202547969084796e-25)

評估分配選擇：tc 血清

results = []
list_of_dists = ["laplace", "norm", "expon"]

for i in list_of_dists:
    dist = getattr(st, i)
    param = dist.fit(dia["tc"])
    result = st.kstest(dia["tc"], i, args=param)
    print(result)

依序為 Laplace、常態與指數分配的結果：

KstestResult(statistic=0.06435779928393615, pvalue=0.04915329841106708)
KstestResult(statistic=0.051165295747227724, pvalue=0.19085587687385897)
KstestResult(statistic=0.3318461436889846, pvalue=7.018486943525e-44)

一起來練習吧！

Python 的 Monte Carlo 模擬