This book is an extension of the author's first book and serves as a guide and manual on how to
specify and compute 2- 3- and 4-Event Bayesian Belief Networks (BBN). It walks the learner
through the steps of fitting and solving fifty BBN numerically using mathematical proof. The
author wrote this book primarily for inexperienced learners as well as professionals while
maintaining a proof-based academic rigor. The author's first book on this topic a primer
introducing learners to the basic complexities and nuances associated with learning Bayes'
theorem and inverse probability for the first time was meant for non-statisticians unfamiliar
with the theorem-as is this book. This new book expands upon that approach and is meant to be a
prescriptive guide for building BBN and executive decision-making for students and
professionals intended so that decision-makers can invest their time and start using this
inductive reasoning principle in their decision-making processes. It highlights the utility of
an algorithm that served as the basis for the first book and includes fifty 2- 3- and
4-event BBN of numerous variants.
