Bayesian Methods: From Theory to Real-World Applications

Author(s): Shenggang Li

Originally published on Towards AI.

A Practical Guide to Using Bayesian Techniques in A/B Testing and Uplift Modeling

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The Bayesian approach is commonly applied in fields such as finance, marketing, and medicine. In this paper, I focus on how it can be used for A/B testing.

I begin with a simple example to explain Bayes’ Theorem and show how it helps handle uncertainty and make better decisions.

Next, I discuss how Bayesian testing differs from traditional methods like t-tests and why it can be more effective for analyzing A/B test results.

The main part of the paper explores Bayesian A/B testing in marketing, particularly with uplift models, comparing its performance to traditional approaches.

Finally, I share the results and business insights, highlighting how Bayesian methods support smarter decision-making in uncertain situations.

I will use a Tall Tale of Students and Sports Clubs to explain Bayesian theory.

If you’re familiar with Bayesian theory, jump to the next section.

Imagine this: I’m a student at a large school with hundreds of fellow students. There’s a popular sports club on campus that everyone is buzzing about, but only 5% of the students join. So, out of every 100 students, only about 5 are members of this club.

Here’s something interesting about the sports… Read the full blog for free on Medium.

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