70% True and 30% False - An Introduction

Much of my education (a Masters degree in Physics) and my career has focused on finding "the answer" to problems. However, in the last fifteen years or so, I have discovered Bayes' theorem, that allows not only a true-false answer, but a probability: 70% true and 30% false.

My plan is to talk about my journey in applying Bayes' theorem to the problems that I often face as a professional. For example, when fracturing a well, we would predict a single number for the fracture created by the fluid injected into the well under high pressure. It is satisfying to get a single answer to a question, but that single answer may not be the best answer when it comes to making a decision.

Bayes' theorem provides a mathematical treatment of uncertainty; as a physicist, I like mathematical treatments. But by providing a probabilistic model of "the answer", Bayes' theorem allow us to not only to improve that answer with additional data but also to make better decisions by consider possibilities beyond "Yes" and "No."

I would love to tell you that I have all the answers about applying Bayes' theorem to physics, engineering, and commercial problems, but I do not. However, I believe that we can learn together not merely the mathematics of Bayes' theorem but its application to decisions today.

I hope not merely to document my learning in this application, but to learn together. Come with me to find out how to effectively apply answers like "70% true and 30% false" to the daily problems we face as we seek to understand and thrive in our incredibly uncertain world.