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A/B Testing and Multi-Armed Bandits with R: Build High-Converting Experiments with Sequential Testing, Thompson Sampling, and Ucb
Paperback
Series: Real-World Data Science with R, Book 5
General ComputersGeneral SociologyProbability & Statistics
Publisher: Independently Published
Published: Mar 24 2026
Pages: 132
Weight: 0.41
Height: 0.28 Width: 6.00 Depth: 9.00
Language: English
A/B Testing and Multi-Armed Bandits with R
Build High-Converting Experiments with Sequential Testing, Thompson Sampling, and UCB
Most A/B tests fail where it matters most in real-world systems where timing, traffic, and uncertainty cannot be controlled.
If you are still running fixed experiments and waiting weeks for results, you are already losing performance, revenue, and learning opportunities.
This book shows you how to move beyond static testing into adaptive experimentation systems that learn and optimize in real time.
Instead of treating experimentation as a one-time analysis, you will learn how to build systems that:
- Continuously allocate traffic to better-performing variants
- Adapt instantly to changing user behavior
- Make statistically valid decisions without waiting for fixed sample sizes
- Scale from simple tests to full production decision engines
Inside this book, you will learn how to:
Build A/B testing pipelines in R that are production-readyImplement multi-armed bandit algorithms including epsilon-greedy, UCB, and Thompson Sampling
Apply sequential testing methods without inflating false positives
Use Bayesian A/B testing for probability-based decision making
Design contextual bandits for real-time personalization
Simulate and validate strategies before deployment
Scale experimentation systems with low latency and high reliability
Transition from testing frameworks to continuous optimization systems
This is not a theory-heavy statistics book. Every concept is tied to real-world implementation, system design, and decision-making under uncertainty.
Whether you are working in product analytics, data science, growth optimization, or machine learning, this book gives you the tools to build systems that learn faster and perform better.
If you want to stop running slow experiments and start building systems that optimize continuously, this book delivers the framework to do it right.
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