An Introduction to Bayes’ Theorem and Bayesian Belief Networks (BBN)
The theory behind BBN, i.e., Bayes’ theorem, is becoming increasingly applicable in economic decision-making in today’s human capital and economic markets across all business, government, and commercial segments on the new global economy. The economic end state of these markets is clearly to maximize stakeholder wealth effectively and efficiently. The question remains, are we? In an attempt to respond to this question, this chapter provides a discussion and an introduction to Bayes’ Theorem and BBN, the identification of the truth, the motivation for this book, the intent of this book, the utility of Bayes’ theorem, inductive verses deductive logic, Popper’s logic of scientific discovery, and frequentist verses Bayesian (subjective) views, to include a discussion on frequentist to subjectivist and Bayesian philosophy.
I would like to thank the following researchers for their contributions to this chapter: (1) Anna T. Cianciolo, Ph.D., (2) Stefan Conrady, (3) Major Tonya R. Tatum, (4) Mrs. Denise Vaught, MBA, and (5) Jeffrey S. Grover Jr. Dr. Cianciolo is an Assistant Professor with the Department of Medical Education, Southern Illinois University School of Medicine, http://www.siumed.edu/; Stefan Conrady is the Managing Partner of Conrady Science, www.conradyscience.com; Major Tatum is an Operations Research/Systems Analyst with the Mission and Recruiter Requirements Division, Assistant Chief of Staff, G2, U.S. Army Recruiting Command; Denise Vaught is the President of Denise Vaught & Associates, PLLC, http://www.denisevaught.com/ and is a Registered Nurse, Certified Rehabilitation Registered Nurse a Certified Case Manager; and Mr. Jeffrey S. Grover Jr. is a Senior and Economics Major at the University of Kentucky, Lexington.
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