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    <title>Big Data on 淳于棼的賭書潑酒</title>
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      <title>大自然不會被嘲笑</title>
      <link>https://yufen-chun.rbind.io/blog/%E5%A4%A7%E8%87%AA%E7%84%B6%E4%B8%8D%E6%9C%83%E8%A2%AB%E5%98%B2%E7%AC%91/</link>
      <pubDate>Fri, 08 Jan 2021 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;倫敦大學瑪麗王后學院的 2 位教授寫了 1 篇文章，題目是 &lt;a href=&#34;https://bit.ly/2JTG4jj&#34;&gt;On false positives in COVID19 testing again: we are being misled over confirmatory testing&lt;/a&gt;。Norman Fenton 是風險信息管理學教授；Martin Neil 是計算機科學和統計學教授。&lt;/p&gt;&#xA;&lt;p&gt;文章內 2 幅圖說得非常清楚：&lt;/p&gt;&#xA;&lt;ol style=&#34;list-style-type: decimal&#34;&gt;&#xA;&lt;li&gt;支持 “封城”所需的證據：“效益”是否超過 “成本”？&lt;/li&gt;&#xA;&lt;li&gt;為什麼每天報告的 Covid-19 數據幾乎什麼都不告訴我們。&lt;/li&gt;&#xA;&lt;/ol&gt;&#xA;&lt;p&gt;&lt;img src=&#34;images/covid_cost_benefit2.jpg&#34; /&gt;&lt;/p&gt;&#xA;&lt;p&gt;&lt;img src=&#34;images/missing_covid_data.jpg&#34; /&gt;&lt;/p&gt;&#xA;&lt;p&gt;Fenton 教授有關 covid-19 的分析的貝葉斯網絡在下面：&lt;/p&gt;&#xA;&#xA;&#xA;    &#xA;    &lt;div style=&#34;position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden;&#34;&gt;&#xA;      &lt;iframe allow=&#34;accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share&#34; allowfullscreen=&#34;allowfullscreen&#34; loading=&#34;eager&#34; referrerpolicy=&#34;strict-origin-when-cross-origin&#34; src=&#34;https://www.youtube.com/embed/3KGYuLFMRSY?autoplay=0&amp;controls=1&amp;end=0&amp;loop=0&amp;mute=0&amp;start=0&#34; style=&#34;position: absolute; top: 0; left: 0; width: 100%; height: 100%; border:0;&#34; title=&#34;YouTube video&#34;&#xA;      &gt;&lt;/iframe&gt;&#xA;    &lt;/div&gt;&#xA;&#xA;&lt;p&gt;進一步可以參考：&lt;/p&gt;&#xA;&lt;p&gt;Giles Wilkes.&#xA;&lt;a href=&#34;https://bit.ly/3ooFjOj&#34;&gt;The doubtful case for an impossible Covid-19 cost-benefit analysis&lt;/a&gt;.&lt;/p&gt;</description>
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      <title>Ch 1 Big Data&#39;s watch eye</title>
      <link>https://yufen-chun.rbind.io/blog-2/ch-1-big-data-s-watch-eye/</link>
      <pubDate>Mon, 28 May 2018 00:00:00 +0000</pubDate>
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      <description>&lt;div id=&#34;TOC&#34;&gt;&#xA;&lt;ul&gt;&#xA;&lt;li&gt;&lt;a href=&#34;#big-datas-watchful-eye&#34; id=&#34;toc-big-datas-watchful-eye&#34;&gt;Big data’s watchful eye&lt;/a&gt;&#xA;&lt;ul&gt;&#xA;&lt;li&gt;&lt;a href=&#34;#what-is-big-data&#34; id=&#34;toc-what-is-big-data&#34;&gt;What is big data?&lt;/a&gt;&lt;/li&gt;&#xA;&lt;li&gt;&lt;a href=&#34;#examples&#34; id=&#34;toc-examples&#34;&gt;Examples&lt;/a&gt;&lt;/li&gt;&#xA;&lt;/ul&gt;&lt;/li&gt;&#xA;&lt;/ul&gt;&#xA;&lt;/div&gt;&#xA;&#xA;&lt;div id=&#34;big-datas-watchful-eye&#34; class=&#34;section level2&#34;&gt;&#xA;&lt;h2&gt;Big data’s watchful eye&lt;/h2&gt;&#xA;&lt;blockquote&gt;&#xA;&lt;p&gt;“The world is full of obvious things which nobody by any chance ever observes.”&lt;/p&gt;&#xA;&lt;p&gt;— Sir Arthur Conan Doyle. Sherlock Holmes: the Hound of the Baskerville, 28 (ebook pub. 2016)&lt;/p&gt;&#xA;&lt;/blockquote&gt;&#xA;&lt;div id=&#34;what-is-big-data&#34; class=&#34;section level3&#34;&gt;&#xA;&lt;h3&gt;What is big data?&lt;/h3&gt;&#xA;&lt;ul&gt;&#xA;&lt;li&gt;Report to the President&lt;/li&gt;&#xA;&lt;/ul&gt;&#xA;&lt;blockquote&gt;&#xA;&lt;p&gt;“There are many definitions of big data which may differ depending on whether you are a computer scientist, a financial analyst, or an entrepreneur pitching an idea to a venture capitalist. Most definitions reflect the growing technological ability to capture, aggregate, and process an ever-greater volume, velocity and variety of data.”&lt;/p&gt;</description>
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