Strategic Economic Decision Making
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Strategic Economic Decision Making

Using Bayesian belief networks to solve complex problems.
1.0 (2 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
94 students enrolled
Last updated 6/2016
English
Price: $95
30-Day Money-Back Guarantee
Includes:
  • 4.5 hours on-demand video
  • 14 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What Will I Learn?
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
View Curriculum
Requirements
  • Microsoft PowerPoint and Microsoft Excel.
Description

Grover Group, Inc. (GGI), offers this course so that learners can use inductive logic when making business decisions that effect an organizations economic outcomes. We base this course on our primer, "A Manual for Strategic Economic Decision-Making: Using Bayesian Belief Networks to make Complex Decisions (2016)," which is an extension of "Strategic Economic Decision-Making: Using Bayesian Belief Networks to make Complex Decisions (Springer, 2013).  This course is a thorough investigation on Bayesian belief networks (BBN), where we will provide the learner with the underlying principles associated with Bayes' theorem and its application to BBN. 

The value of BBNs is that they take an initial guess of probability likelihoods and filter them through observable information to predict future states of nature in the form of posterior probabilities. This course is meant for learners that are non-statisticians and will complement those that have a basic understanding of statistics and Bayes' theorem. During this course, we will walk the learner through the modeling and application of BBN using real-world applications. We will do this by introducing the learner to the underlying principles of discrete mathematics using set theory and discrete axioms of probability, These underlying concepts include counting and subsequent calculation of prior, marginal, likelihood, joint, and finally posterior probabilities.

At the end of the course, the learner will replicate 10 BBNs based on real world problems in the area of economics. We will explain the requirements of fitting a Bayes' model in this course. Upon course completion, the learner can mathematically determine posterior probabilities. These posteriors will represent the initial guess of the investigator.

Very little has been published in the area of discrete Bayes' theory, and this course will appeal to both non-statisticians with little to no knowledge of BBN and statisticians currently conducting research in the fields of engineering, computing, life sciences, and social sciences.

Who is the target audience?
  • CEOs
  • COOs
  • CFOs
  • Statistics students
  • Artificial Intelligence Students
  • Strategic Economic Decision-Makers
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Curriculum For This Course
Expand All 32 Lectures Collapse All 32 Lectures 11:48:22
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Chapter 1: Introduction to Bayes' Theorem and Bayesian Belief Networks
2 Lectures 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.
Preview 27:52

Chapter 1 PowerPoint Slides.
Preview 45 pages
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Chapter 2-A Literature Review of Bayes' Theorem and Bayesian Belief Networks
4 Lectures 30:38
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, ...
Part I-Video-Introduction to Bayes' theorem and Bayesian Belief Networks
13:30

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...
Part II-Video-BBN Evolution
12:32

and a review of current government and commercial users of BBN, references & conclusions
Preview 04:36

Chapter 2 PowerPoint Slides.
Chapter 2-PDF
69 pages
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Statistical Properties of Bayes' Theorem
4 Lectures 39:02
Part I-Video-Introduction & Bayes' Proofs
07:02


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.
Part III-The Algebra of Sets, References, & Conclusions
04:47

Chapter 3-PDF
64 pages
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Experimental Protocol
2 Lectures 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).

Chapter 4-Video
24:05

Chapter 4 PowerPoint Slides.
Chapter 4-PDF
29 pages
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Chapter 5-Manufacturing Example
2 Lectures 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. 

Preview 11:59

Chapter 5 PowerPoint Slides.
Chapter 5-PDF
25 pages
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Political Science Example
2 Lectures 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.

Chapter 6-Video
12:53

Chapter 6 PowerPoint Slides.
Chapter 6-PDF
23 pages
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Gambling Example
2 Lectures 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.

Chapter 7-Video
12:51

Chapter 7 PowerPoint Slides.
Chapter 7-PDF
23 pages
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Publicly Trade Company Example
2 Lectures 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.

Chapter 8-Video
13:38

Chapter 8 PowerPoint Slides.
Chapter 8-PDF
23 pages
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Insurance Level Risks Example
2 Lectures 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.

Chapter 9-Video
13:06

Chapter 9 PowerPoint Slides.
Chapter 9-PDF
23 pages
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Acts of Terrorism Example
2 Lectures 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.

Chapter 10-Video
12:48

Chapter 10 PowerPoint Slides.
Chapter 10-PDF
23 pages
4 More Sections
About the Instructor
Dr. Jeff Grover
1.0 Average rating
2 Reviews
96 Students
3 Courses
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 his recent book, A Manual for Strategic Economic Decision-Making: Using Bayesian Belief Networks to Make Complex Decisions (Springer, 2016), which is an extension of his original 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. Grover is a father of Rebecca Tabb and Jeffrey S. Grover Jr. and is also a retired US Army Special Forces officer.