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This lesson is an excerpt from the published, Strategic Economic Decision-Making: Using Bayesian Belief Networks to Solve Complex Problems (Grover 2013). I have realized that during the literature review how much industry has greatly benefited from the utility of Bayesian Belief Networks (BBN). In fact, my latest Google search using the keyword "Bayesian" populated more than 12,000,000 hits. When I wrote this book, this search term had 4,000,000 hits. I am predicting exponential interest in Bayes' theorem and BBN in the next decade. Now, we have high performance computers and enormous amount of data that is being collected globally by governments and private sector industries, especially Twitter, Facebook, and Google, which is mind-boggling. My goal is to provide the learner with simple proofs to use and reference to learn how to convert this data information into business intelligence and BBN are proving to be the tool of choice in this endeavor. A limitation to learning BBN is that the statistical and computer programming symbology are not straight forward for the naive student to read and comprehend. My concern is that this eloquent concept is not transparent to the human consumer of decision-making processes. The use of Bayes' theorem and inductive logic allows for the embedding of subjective matter expertise as a starting point for executive decision-making and is an indispensable tool in decision theory.
The scope of this course is to explore the constructs of statistical proofs of two, three, and four event Bayesian Belief Networks (BBN). The target audience are those learners who are interested in the mathematical underpinnings of BBN.
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|Section 1: Demostration|
|Section 2: Introduction|
This is the introduction to the series and provides an outline of the content.
|Section 3: Chapter 1|
|Section 4: Chapter 2|
|Section 5: Chapter 3|
|Section 6: Chapter 4|
Dr. Jeff Grover has a Doctor of Business Administration in Finance and is founder and chief research scientist at Grover Group, Inc. (GGI) where he specializes in Bayes’ Theorem and its application through Bayesian belief networks (BBN) to strategic economic decision-making (BayeSniffer.com). At GGI, he specializes in blending economic theory and BBN to maximize stakeholder wealth. He is a winner in the Kentucky Innovation Award Winner (2015) for the application of his proprietary BBN big data algorithm. He has operationalized BBN in the healthcare industry, evaluating the Medicare “Hospital Compare” data; in the Department of Defense, conducting research with U.S. Army Recruiting Command to determine optimal levels of required recruiters for recruiting niche market medical professionals; and in the agriculture industry in optimal soybean selection. In the area of economics, he was recently contracted by the Department of Energy, The Alliance for Sustainable Energy, LLC Management and Operating Contractor for the National Renewable Energy Laboratory, to conduct a 3rd party evaluation of the Hydrogen Financial Analysis Scenario (H2FAST) Tool (2015).
Jeff received his Doctors of Business Administration in Finance from NOVA Southeastern (2003), MBA from ERAU (1997), and a BS in Math from Mobile College (1987).
Jeff has published a book, Strategic Economic Decision-Making: Using Bayesian Belief Networks to Make Complex Decisions with SpringerBriefs (2013). Also, he has published in the Journal of Wealth Management, the Journal of Business and Leadership; Research, Practice, and Teaching, and the Journal of Business Economics Research. Recently, He was a guest speaker at the MORS Conference in Washington, DC (12/2014) where he gave a presentation on the application of BBN in the area of terrorism.
Dr. Groveris a father of Rebecca Tabb and Jeffrey S. Grover Jr. and is also a retired US Army Special Forces officer (2001).