This sign appears in the Cambridge UK office of Autonomy Corporation. I want one. I need to talk to the people who make neon signs. There are a few online threads (1, 2) where people express curiosity about this sign.
This equation is Bayes' Law. Thomas Bayes (1701-1761) proposed it as a way to update one's beliefs based on new information. I saw this picture on a blog post by Allen Downey, author of Think Bayes, and whom I recently had the pleasure of meeting briefly at a Boston Python meetup. Very interesting guy, also well versed in digital signal processing, another interest shared with myself. Before the other night, I probably hadn't heard the word "cepstrum" in almost twenty years.
Allen's blog is a cornucopia of delicious problems involving Bayes' Law and other statistical delights that I learned to appreciate while taking 6.432, an MIT course on detection and estimation that I'm afraid may have been retired. The online course materials they once posted for it have been taken down.
But imagine my satisfaction upon looking over Think Bayes and realizing that it is the missing textbook for that course! I haven't checked to see that it covers every little thing that was in 6.432, but it definitely covers the most important ideas. At a quick glance, I don't see much about vectors as random variables, but I think he's rightly more concerned with getting the ideas out there without the intimidation of extra mathematical complexity.
This equation is Bayes' Law. Thomas Bayes (1701-1761) proposed it as a way to update one's beliefs based on new information. I saw this picture on a blog post by Allen Downey, author of Think Bayes, and whom I recently had the pleasure of meeting briefly at a Boston Python meetup. Very interesting guy, also well versed in digital signal processing, another interest shared with myself. Before the other night, I probably hadn't heard the word "cepstrum" in almost twenty years.
Allen's blog is a cornucopia of delicious problems involving Bayes' Law and other statistical delights that I learned to appreciate while taking 6.432, an MIT course on detection and estimation that I'm afraid may have been retired. The online course materials they once posted for it have been taken down.
But imagine my satisfaction upon looking over Think Bayes and realizing that it is the missing textbook for that course! I haven't checked to see that it covers every little thing that was in 6.432, but it definitely covers the most important ideas. At a quick glance, I don't see much about vectors as random variables, but I think he's rightly more concerned with getting the ideas out there without the intimidation of extra mathematical complexity.
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