{"url":"https://tishow.hateblo.jp/entry/2018/06/19/A/B_test_with_python_%28Bandit%29","provider_name":"Hatena Blog","author_name":"T_I_SHOW","published":"2018-06-19 07:20:34","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Ftishow.hateblo.jp%2Fentry%2F2018%2F06%2F19%2FA%2FB_test_with_python_%2528Bandit%2529\" title=\"A/B test with python (Bandit) - \u9650\u308a\u306a\u304f\u9662\u751f\u306b\u8fd1\u3044\u30d1\u30ea\u30d4\uff20\u30a8\u30b9\u30c8\u30cb\u30a2\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","blog_url":"https://tishow.hateblo.jp/","description":"In this article, I am going to write a Bayesian Bandit algorithm. import matplotlib.pyplot as plt import numpy as np from scipy.stats import beta NUM_TRIALS = 2000 BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] Bandit probabilities are divided into three value just in case. Quick favorite. Trial number is \u2026","image_url":null,"title":"A/B test with python (Bandit)","author_url":"https://blog.hatena.ne.jp/T_I_SHOW/","width":"100%","provider_url":"https://hatena.blog","categories":["DataScience","Statistics","python"],"version":"1.0","blog_title":"\u9650\u308a\u306a\u304f\u9662\u751f\u306b\u8fd1\u3044\u30d1\u30ea\u30d4\uff20\u30a8\u30b9\u30c8\u30cb\u30a2","height":"190","type":"rich"}