{"html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fenakai00.hatenablog.com%2Fentry%2F2016%2F03%2F17%2F103542\" title=\"PRML6.4.2 Gaussian processes for regression\u306e\u30e1\u30e2 - \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>","image_url":"http://cdn-ak.f.st-hatena.com/images/fotolife/e/enakai00/20160317/20160317100215.png","type":"rich","width":"100%","blog_title":"\u3081\u3082\u3081\u3082","provider_name":"Hatena Blog","description":"\u57fa\u5e95\u3092\u56fa\u5b9a\u3057\u305f\u5834\u5408\u306e\u8a08\u7b97\u91cf \u4e00\u822c\u306b\u306f\u3001N\u2715N\u884c\u4f8b\u306e\u9006\u884c\u5217\u8a08\u7b97\u304c\u5fc5\u8981\u306b\u306a\u308b\u3068\u3053\u308d\u304c\u3001\u6709\u9650\u500b\u306e\u57fa\u5e95\u3092\u56fa\u5b9a\u3059\u308b\u3068M\u2715M\u884c\u5217\u306e\u9006\u884c\u5217\u8a08\u7b97\u306b\u8a08\u7b97\u91cf\u304c\u6e1b\u5c11\u3059\u308b\u7406\u7531\u3092\u5177\u4f53\u7684\u306a\u8a08\u7b97\u3067\u78ba\u8a8d\u3002 Figure 6.10\u3092\u518d\u73fe\u3059\u308b\u30b3\u30fc\u30c9 \u308f\u304b\u308a\u3084\u3059\u3055\u512a\u5148\u3067\u5197\u9577\u306a\u30b3\u30fc\u30c9\u306b\u3057\u3066\u3042\u308a\u307e\u3059\u3002 # -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt from numpy.random import normal np.random.seed(20160317) num_data = 100 iter_num = 50 rate = 0\u2026","url":"https://enakai00.hatenablog.com/entry/2016/03/17/103542","author_url":"https://blog.hatena.ne.jp/enakai00/","provider_url":"https://hatena.blog","blog_url":"https://enakai00.hatenablog.com/","categories":[],"height":"190","title":"PRML6.4.2 Gaussian processes for regression\u306e\u30e1\u30e2","published":"2016-03-17 10:35:42","author_name":"enakai00","version":"1.0"}