Bayes' Theorem: Definition, Formula & Usage Video

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  • 0:00 More Accurate Predictions
  • 0:49 Bayes' Theorem
  • 1:36 Using the Math
  • 2:43 Example
  • 3:38 Lesson Summary
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Lesson Transcript
Instructor: Kevin Newton

Kevin has edited encyclopedias, taught middle and high school history, and has a master's degree in Islamic law.

Using Bayes' Theorem allows you to revise probability values when making decisions. This lesson explains what Bayes' Theorem is and how it is applied in decision making.

More Accurate Predictions

In business, being able to predict the future is no small part of succeeding. No, I'm not suggesting that you resort to crystal balls or tarot cards. In fact, those would probably prove to do more harm than good! However, being able to make the most accurate predictions possible based off of a set of facts is a key skill. That is one of the reasons that Bayes' Theorem is so useful. Unlike simple probability, which only tests the probability of unrelated events, Bayes' theorem allows us to run probability tests on dependent events. This allows us to more accurately base predictions on required events which themselves are not guaranteed. In this lesson, we're going to learn how to use Bayes' theorem, including a walk through of the math involved, before trying it all out in a real-world example.

Bayes' Theorem

Before we move on, I want to be sure that you really get just how amazing of a thing this is for business. Before Bayes, it was impossible to really get a handle on how related events would interact. Since so much in business follows on established events, being able to calculate the probability of events makes business predictions so much more accurate.

Not only does Bayes' Theorem allow us to check for future events but also control for false positives. While not as much of a concern in the business world, the scientific and healthcare uses of Bayes' theorem allows for people to calculate the chance that someone identified as a positive match by a test could indeed be a false positive. As you might expect, this information is itself very useful in diagnoses of patients.

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