Strategic Economic Decision-Making

This is a "How to" use Bayes' theorem using Bayesian Belief Networks to solve complex problems.
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  • Lectures 32
  • Contents Video: 4.5 hours
    Other: 7.5 hours
  • Skill Level All Levels
  • Languages English
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About This Course

Published 4/2013 English

Course Description

This course is about using deductive logic when making strategic economic decisions that effect an organizations economic output. This is based on my book, "Strategic Economic Decision-Making: Using Bayesian Belief Networks to make Complex Decisions. This is a quick primer on the topic that introduces readers to the basic complexities and nuances associated with learning Bayes' theory and inverse probability for the first time. This brief is meant for non-statisticians who are unfamiliar with Bayes' theorem, walking them through the theoretical phases of set and sample set selection, the axioms of probability, probability theory as it pertains to Bayes' theorem, and posterior probabilities. All of these concepts are explained as they appear in the methodology of fitting a Bayes' model, and upon completion of the text readers will be able to mathematically determine posterior probabilities of multiple independent nodes across any system available for study. Very little has been published in the area of discrete Bayes' theory, and this brief will appeal to non-statisticians conducting research in the fields of engineering, computing, life sciences, and social sciences.

What are the requirements?

  • Microsoft PowerPoint and Excel and Netica Software from www.Norsys.com

What am I going to get from this course?

  • To learn how to proof Bayes' theorem
  • To learn about prior probabilities in the context of Bayesian Belief Networks
  • To learn about likelihood probabilities in the context of Bayesian Belief Networks
  • To learn about joint probabilities in the context of Bayesian Belief Networks
  • To learn about marginal probabilities in the context of Bayesian Belief Networks
  • To learn about conditional probabilities in the context of Bayesian Belief Networks

What is the target audience?

  • CEOs
  • COOs
  • CFOs
  • Statistics students
  • Artificial Intelligence Students
  • Strategic Economic Decision-Makers

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

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Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Chapter 1: Introduction to Bayes' Theorem and Bayesian Belief Networks
27:52
Abstract 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.
45 pages
Chapter 1 PowerPoint Slides.
Section 2: Chapter 2-A Literature Review of Bayes' Theorem and Bayesian Belief Networks
13:30
Abstract This chapter provides an introduction to the Bayes’ theorem evolution to include: a) the early 1900s, b) 1920-1930s, c) 1940-1950s, and d) 1960s-Mid 1980s, ...
12:32
7a review of the BBN evolution to include: a) financial economics, accounting, and operational risks, b) safety, accident analysis, and prevention, c) engineering and safety risk analysis, d) ecology, e) human behavior, f) behavioral sciences and marketing, g) decision support systems with expert systems and applications, information sciences, intelligent data analysis, neuroimaging, environmental modeling and software, and industrial ergonomics, h) cognitive science, i) medical, health, dental, and nursing, j) environmental studies, and k) miscellaneous—politics, geriatrics, space policy, and language and speech...
04:36
and a review of current government and commercial users of BBN, references & conclusions
69 pages
Chapter 2 PowerPoint Slides.
Section 3: Statistical Properties of Bayes' Theorem
Part I-Video-Introduction & Bayes' Proofs
07:02
Part II-Video-Statistical Definitions
Preview
27:13
04:47
4) the algebra of sets including: Theorem 1: for any Subsets, A, B, & C of a set U and Theorem 2: for any Subsets, A and B of a set U.
Chapter 3-PDF
64 pages
Section 4: Experimental Protocol
24:05

Abstract  This chapter provides an introduction to BBN experimental protocol, the experimental protocol for BBN, the  characteristics of a random experiment, and the conduct a Bayesian experiment, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication.[1]



[1] See the Norsys Software Corp. website (Netica 2012).

29 pages
Chapter 4 PowerPoint Slides.
Section 5: Chapter 5-Manufacturing Example
11:59

Abstract This chapter highlights an example of a Bayesian Belief Network (BBN) in a manufacturing scenario by evaluating the variables of “Transistor Quality” and “Suppliers.” Here, quality control and costs have great utility in the company’s ability to make a profit, gain a competitive advantage, and maintain their reputation as an industry leader. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario. 

25 pages
Chapter 5 PowerPoint Slides.
Section 6: Political Science Example
12:53

Abstract This chapter highlights an example of Bayesian Belief Network (BBN) in a national economic scenario by evaluating the variables of “County” and “Political Affiliation.” Here, the balance between Democrats and Republicans has great utility in a politician’s ability to determine her or his position in the political arena to maintain their reputation as a government servant. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario.

23 pages
Chapter 6 PowerPoint Slides.
Section 7: Gambling Example
12:51

Abstract This chapter highlights an example of Bayesian Belief Network (BBN) in a gaming scenario by evaluating the variables of “Die Randomness” and “Fair Die.” Here, the balance between winning and losing has great utility in a casino’s ability to remain profitable while hedging risk to maintain their reputation as a gaming establishment. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario.

23 pages
Chapter 7 PowerPoint Slides.
Section 8: Publicly Trade Company Example
13:38

Abstract This chapter highlights an example of Bayesian Belief Network (BBN) in a national economic scenario by evaluating the variables of “Altman Z-Scores” and “Health Status.” Here, the balance between international company default and investments has great economic utility in a country’s ability to warn its international investors and to hedge global effects of default to maintain their reputation as a global financial leader. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario.

23 pages
Chapter 8 PowerPoint Slides.
Section 9: Insurance Level Risks Example
13:06

Abstract This chapter highlights an example of Bayesian Belief Network (BBN) in an insurance scenario by evaluating the variables of “Risk Category” and “Fatality Status.” Here, the balance between risk and premiums have great economic utility in the company’s ability to make a profit, gain a competitive advantage, and maintain their reputation as an industry leader. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario.

23 pages
Chapter 9 PowerPoint Slides.
Section 10: Acts of Terrorism Example
12:48

Abstract This chapter highlights an example of Bayesian Belief Network (BBN) in a potential hostile scenario by evaluating the variables of “Country” and “Fatality Status.” Here, citizen safety has great economic utility in a person’s ability to maintain safety while living and traveling abroad in countries with terrorist cells who desire to launch their economic and political agenda on innocent citizens. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario.

23 pages
Chapter 10 PowerPoint Slides.
Section 11: Currency Wars Example
14:24

Abstract This chapter highlights an example of Bayesian Belief Network (BBN) in an investment scenario by evaluating the variables of “Currency Pair” and “Economic Effects.” Here, the economic value of currency has great economic utility in a country’s ability to provide the correct economic balance of goods and services to maintain their reputation as a solvent nation within the global free-market economy where any interference in this process could have grave consequences. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario.

25 pages
Chapter 11 PowerPoint Slides.
Section 12: College Entrance Exams Example
13:08

Abstract This chapter highlights an example of Bayesian Belief Network (BBN) in an academic scenario by evaluating the variables of “Freshman Status” and “ACT Scores.” Here, student retention has great human capital economic utility in the university’s ability to make profit and maintain accreditation and high academic standards to maintain their reputation as an industry leader. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario.

23 pages
Chapter 12 PowerPoint Slides.
Section 13: Special Forces Assessment and Selection-One Stage Model Example
12:14

Abstract This chapter highlights an example of Bayesian Belief Network (BBN) in a military scenerio by evaluating the variables of “Graduate” and “Status.” Here, SFAS has great human capital economic utility in the U.S. Army’s ability to provide the special operations communities with the finest candidates available in a limited pool of Soldiers. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario.

25 pages
Chapter 13 PowerPoint Slides.
Section 14: Special Forces Assessment and Selection-Two Stage Model Example
18:44

Abstract This chapter is an extension of Chapter 13, Special Forces Assessment and Selection (SFAS ) One-Stage Bayesian Belief Network (BBN) Example, and highlights an example of a BBN in a military scenario by evaluating the variables of “Graduate,” “Status,” and “PT” (Physical Fitness Levels). Here, SFAS has great human capital economic utility in the U.S. Army’s ability to provide the special operations communities with the finest candidates available in a limited pool of Soldiers. It also provides the experimental protocol for conducting the BBN, which includes the following 11-Steps: a) Step 1: identify a population of interest, b) Step 2: slice through this population and identify at a minimum two mutually exclusive or disjoint (unconditional) events, which are the subsets of our population, c) Step 3: determine prior (a priori) or unconditional probabilities, d) Step 4: identify the conditional event and its subset of mutually exclusive or disjoint (unconditional) elements, e) Step 5: conduct the random experiment, f) Step 6: determine frequency counts, g) Step 7: determine likelihood/conditional probabilities (relative frequencies), h) Step 8: determine joint probabilities, i) Step 9: determine posterior probabilities, j) Step 10: draw a tree diagram, and k) Step 11: run a Netica replication. In addition, it provides a conclusion, which includes a discussion of posterior and inverse probabilities as they pertain to this scenario.

31 pages
Chapter 14 PowerPoint Slides.

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Instructor Biography

Dr. Jeff Grover, Solving Complex Problems, LINK by LINK.

  

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).

 

 

 

      

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