Bayesian Model for A/B Testing Using Python

Показать описание

A Bayesian model for A/B testing allows you to use Bayes' theorem to update the probability of hypotheses as new data becomes available, offering a probabilistic approach to decision-making. In this context, prior knowledge is combined with new data to form updated beliefs, expressed as posterior distributions, which are used to assess and compare different strategies. For instance, in testing two pricing strategies (A and B), the model calculates the posterior probabilities of revenue for each strategy, incorporating prior beliefs about revenue distributions. By simulating posterior samples, the model can estimate the likelihood of one strategy outperforming the other. The model's advantage lies in continuous updates and actionable probabilities, allowing for better-informed decisions. In addition to calculating probabilities such as the likelihood of one strategy being better than the other, it also helps visualize the results through posterior distributions. This approach is flexible, offering insights into decision thresholds like revenue targets or return on investment (ROI). Bayesian A/B testing avoids the pitfalls of traditional p-value testing by providing a more nuanced view of uncertainty. Additionally, the model can be applied to a range of business decisions, such as launching new products or allocating marketing budgets, by updating probabilities as more data is collected. In conclusion, Bayesian models offer significant advantages in real-time decision-making, providing more interpretability and flexibility compared to traditional A/B testing methods.

Data Science, Machine Learning, and Python

Рекомендации по теме

Комментарии

Please watch the video in its entirety to get the full effect of the lesson being taught here. Also, go ahead and hit the 'Subscribe' button to be notified of all the new content that I will be dropping in the coming weeks and months.

My goal is to put out 365 videos in 365 calendar days. I started this journey on August 8th, 2024. I am planning to create and release at least 365 videos by August 8th, 2025.

Finally, if you have any requests for instructional/educational videos you would like to see, please post them in the comments section here.

Thanks for your constant support!!!

Straight-Data-Science

You can download the source code, as an HTML file, from here:

Straight-Data-Science

Bayesian Model for A/B Testing Using Python

Bayesian Model for A/B Testing Using Python

Easy as ABC: A Quick Introduction to Bayesian A/B Testing in Python (Will Barker)

Bayesian Machine Learning in Python: A/B Testing

Bayesian A/B Testing - Marc Garcia

How do I do Bayesian AB Testing_Probabilistic Programming Primer

Bayesian vs. Frequentist Statistics ... MADE EASY!!!

VWO SmartStats - Bayesian A/B testing statistics engine

Nora Neumann - Usable A/B testing – A Bayesian approach

Ruben Mak | Successfully applying Bayesian statistics to A/B testing in your business

Let's do some LIVE A/B test Analysis with Bayesian Statistics. [CRO / Experimentation]

Bayes theorem, the geometry of changing beliefs

Bayesian Machine Learning in Python: A/B Testing Intro

Bayesian A/B Testing

A/B testing - Statistical significance for beginners

L5: Bayesian AB Testing (State of Bayes Lecture Series)

A/B Testing for Game Design Iteration: A Bayesian Approach

Are you Bayesian or Frequentist?

Dennis Bohle, Ben Teeuwen - Realtime Bayesian A-B testing with Spark Streaming

AB Testing and Bayesian Stats Lesson

How to run A/B Tests as a Data Scientist!

LSML-7-Bayesian AB Testing Large-Scale Machine Learning

PyCon PL 2014 'Bayesian A/B testing with Python' [EN]

Talking Bayes to Business: A/B Testing Use Case | Shopify

Bayesian Approach to A/B Testing by Varun Panicker