{"html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fenakai00.hatenablog.com%2Fentry%2F2016%2F02%2F28%2F120612\" title=\"PRML Figure6.5\u3092\u518d\u73fe\u3059\u308b\u30b3\u30fc\u30c9 - \u3081\u3082\u3081\u3082\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","author_name":"enakai00","url":"https://enakai00.hatenablog.com/entry/2016/02/28/120612","provider_url":"https://hatena.blog","categories":[],"provider_name":"Hatena Blog","version":"1.0","description":"\u3053\u308c\u3067\u3059\u3002 import numpy as np import matplotlib.pyplot as plt from numpy.random import multivariate_normal params = [(1,4,0,0), (9,4,0,0), (1,64,0,0), (1,0.25,0,0), (1,4,10,0), (1,4,0,5)] fig = plt.figure() for n in range(len(params)): (p0, p1, p2, p3) = params[n] linex = np.linspace(-1,1,999) kern = np.\u2026","image_url":"http://cdn-ak.f.st-hatena.com/images/fotolife/e/enakai00/20160228/20160228115355.png","width":"100%","title":"PRML Figure6.5\u3092\u518d\u73fe\u3059\u308b\u30b3\u30fc\u30c9","blog_title":"\u3081\u3082\u3081\u3082","author_url":"https://blog.hatena.ne.jp/enakai00/","blog_url":"https://enakai00.hatenablog.com/","published":"2016-02-28 12:06:12","type":"rich","height":"190"}