Bayesian A/B Decision Models encapsulates J Christopher Westland's deep expertise in algorithmic decision-making, honed through years of experience in specialized fields like auditing and serving as an expert witness in computing damages for corporate and industrial legal cases. While traditional statistical methods provide valuable insights, they often fall short of capturing the complexities of real-world scenarios that require sophisticated decision-making. Enter the Bayesian A/B decision model — a powerful alternative that offers the flexibility and adaptability essential for navigating the intricate demands of consultancy and legal expert roles.

Westland has found the versatility of Bayesian A/B models to be particularly transformative, enabling him to respond effectively to diverse and dynamic requests for detailed analytical reports. This approach allows for the delivery of precise financial analyses and outcomes that are highly tailored and often beyond the reach of traditional methods. In the high-stakes arenas of legal and corporate deliberations, where the accuracy and adaptability of statistical analysis can be the tipping point, this advantage has proven invaluable.

In Bayesian A/B Decision Models, Westland extends the application of Bayesian A/B testing across a wide array of fields, including healthcare, marketing, and finance, as well as in more specialized sectors where traditional A/B testing and Frequentist approaches may be insufficient due to their limitations. The book features a comprehensive technical appendix with complete computer code for all examples discussed, offering readers practical tools that can be customized and implemented in their work.

Whether you're an experienced statistician, an emerging analyst, or a professional exploring the potential of Bayesian A/B testing, this book provides a thorough overview and detailed applications that highlight the unique benefits of this innovative statistical approach.

Contents:

• 
  

  What are A/B Decision Models?:

  
  
  
  
  • Claude Hopkins' Legacy
  
  
  • What is A/B Testing?
  
  
  • Frequentists and Bayesians Approach A/B Decision Models
  
  
  • Notable A/B Testing Successes
  
  
  • Bayesian A/B Testing
  
  
  • Moving Forward
  
  
  
  


• 
  

  Twelve Questions You Should Ask in Advance of A/B Testing:

  
  
  
  
  • Can You Replicate the Same Decision with New or Updated Data?
  
  
  • How Will You Interpret the Results of Your A/B Analysis in Terms that are Relevant to Your Original Problem?
  
  
  • How Do You Choose the Right Power, Confidence, Effect Size and Significance Level for an A/B Test?
  
  
  • What Can You Do If Data Collection is Constrained by Budget, Data Availability or Other Restrictions?
  
  
  • How Much Information is Actually in Your Dataset?
  
  
  • Have You Found Evidence of Absence, or Just an Absence of Evidence?
  
  
  • How Will You Handle Sequential Tests and Streaming Data Updates?
  
  
  • Am I 'Peeking'?
  
  
  • Is All the Information Relevant to My Decision Captured in My Dataset?
  
  
  • How Should I Treat the Population Parameter for Each Variant?
  
  
  • Have You Changed the Streaming Data Allocations During the Testing Period?
  
  
  • Does Your A/B Modeling Strategy Reflect Your Organization's Strategy?
  
  
  
  


• 
  

  Understanding Prior Distributions:

  
  
  
  
  • What is a 'Probability'?
  
  
  • Epistemic Probability
  
  
  • The Secret Lives of Information
  
  
  • Lies, Damned Lies, and Statistics
  
  
  • Formal Strategies for Choosing Priors
  
  
  • Practical Strategies for Choosing Priors
  
  
  • The World in Six Distributions
  
  
  • Weak and Strong Priors
  
  
  • Applications
  
  
  
  


• 
  

  Posterior Distributions:

  
  
  
  
  • Which Posterior Should I Use?
  
  
  • The Economic Value of a Decision
  
  
  • Commonly Used Loss Functions
  
  
  • Risk Management
  
  
  • Pooling Information
  
  
  • Case Study: The Movie Critics
  
  
  
  


• 
  

  Financial Application of Bayesian A/B Decision Models:

  
  
  
  
  • Application Areas for A/B Decisions in Finance
  
  
  • Advantages of Bayesian A/B Decision Models
  
  
  • Caveats and Pitfalls in Bayesian
  
  
  • Case Study: Cryptobros', Inc. Portfolio Choice
  
  
  • Case Study: Locust Lane Investments Explores National Economic Performance with a Decision Tree of A/B Tests
  
  
  
  


• 
  

  Marketing Applications of A/B Testing:

  
  
  
  
  • Application Areas for A/B Decisions in Marketing
  
  
  • Advantages of Bayesian A/B Decision Models
  
  
  • Caveats and Pitfalls in Bayesian Approaches
  
  
  • Case Study: Impact of User Interface Design on Luxehome Decor's Product Sales
  
  
  • Case Study: Google Analytics at Dashuju.Com
  
  
  
  


• 
  

  A/B Testing in Insurance, Warranties and Risk Management:

  
  
  
  
  • Application Areas for A/B Decisions in Insurance, Warranties and Risk Management
  
  
  • Advantages Offered by Bayesian A/B Decision Models
  
  
  • Caveats and Pitfalls in Bayesian Approaches
  
  
  • Case Study: The Cost of Pet Insurance
  
  
  • Case Study: The Sexy Son
  
  
  
  


• 
  

  Bayesian A/B Decision Models for Sentiment Analysis on Textual Databases:

  
  
  
  
  • Application Areas for A/B Decision Models in Natural Language Processing
  
  
  • Advantages Offered By Bayesian A/B Decision Models
  
  
  • Caveats and Pitfalls in Bayesian Approaches
  
  
  • Why Don't We Use Large Language AI Models for A/B Tests?
  
  
  • The Bag-of-Words Model
  
  
  • Case Study: A/B Testing in Product Design at Madcap Macaroons
  
  
  
  


• 
  

  Legal Applications of A/B Decision Models:

  
  
  
  
  • Bayesian A/B as an Alternative Model for Event Studies
  
  
  • Applications of A/B Testing in Law
  
  
  • Caveats
  
  
  • Case Study: Vinluxe Innovations Sues an Intellectual Property Thief
  
  
  
  


• 
  

  Decisions with More Than Two Choices:

  
  
  
  
  • Bayesian Bandits
  
  
  • Case Study: Bandits at Research Arms (A/B/C Decision Models)
  
  
  • Secure Databases Using a Real-Time Dashboard
  
  
  
  


• 
  

  Final Thoughts

  
  


• 
  

  Technical Appendix:

  
  
  
  
  • R Code for Chapter 1
  
  
  • R Code for Chapter 2
  
  
  • R Code for Chapter 3
  
  
  • R Code for Chapter 4
  
  
  • R Code for Chapter 5
  
  
  • R Code for Chapter 6
  
  
  • R Code for Chapter 7
  
  
  • R Code for Chapter 8
  
  
  • R Code for Chapter 9
  
  
  • R Code for Chapter 10
  
  
  • R Code for Chapter 11
  
  
  
  

Readership: Finance professionals seeking to optimize strategies through data-driven insights; marketing experts enhancing strategies and customer engagement with A/B testing; insurance and risk management professionals improving decision-making and risk mitigation; legal experts applying data-driven methods for damage calculation and risk assessment; and production managers boosting efficiency through A/B testing. Academics and research institutions explore A/B testing applications and its impact across industries. Technology and data science enthusiasts leverage NLP and AI to enhance digital marketing, customer relationship management, and data analysis techniques.