Still Explaining Bayes

March 4, 2014

Bayes’s Theorem is the founding basis for predictive analytics. Gigaom’s article tries to explain how not only Bayes’s Theorem is used in predictive analytics, but there is another factor: “How the Solution To the Monty Hall Problem Is Also The Key To Predictive Analytics.”

The Monty Hall Problem is named after the Let’s Make a Deal host. Here is how it works:

“The show used what came to be known as the Monty Hall Problem, a probability puzzle named after the original host. It works like this: You choose between three doors. Behind one is a car and the other two are Zonks. You pick a door – say, door number one – and the host, who knows where the prize is, opens another door – say, door number three – which has a goat. He then asks if you want to switch doors. Most contestants assume that since they have two equivalent options, they have a 50/50 shot of winning, and it doesn’t matter whether or not they switch doors. Makes sense, right?”

If a data scientist had been on the show, he would have used Bayes’s Theorem to win the prize. The solution is to switch doors.

The Monty Hall Problem is used in business, but Bayes’s Theorem is becoming more widespread. It is used to link big data and cloud computing, which also powers predictive analytics. What follows is an explanation of the theorem’s importance and impact on business, which is not new. It ends with encouraging people to rely on Bayes over Monty Hall.

What will the next metaphor comparison be?

Whitney Grace, March 04, 2014
Sponsored by ArnoldIT.com, developer of Augmentext

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