The Manual for Strategic Economic Decision Making
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# The Manual for Strategic Economic Decision Making

Using Bayesian Belief Networks to Solve Complex Problems
0.0 (0 ratings)
3 students enrolled
Created by Dr. Jeff Grover
Last updated 7/2017
English
Current price: \$10 Original price: \$95 Discount: 89% off
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Includes:
• 32 mins on-demand video
• 8 Articles
• 4 Supplemental Resources
• Access on mobile and TV
• Certificate of Completion
What Will I Learn?
• Construct 2, 3, & 4 node Bayesian Belief Networks.
View Curriculum
Requirements
• Students will need a basic knowledge of algebra and discrete math.
Description

This course will teach the learner how to create and use Bayesian Belief Networks using count data and inductive logic. It will walk the learner through counting, then calculating likelihood, joint & marginal, and then finally posterior probabilities.

1. What is THIS COURSE ABOUT? This course is about using Bayesian Belief Networks to make complex decisions.
2. WHAT TERMINOLOGY CAN YOU EXPECT TO FIND? The terminology will include basic discrete statistical constructs such as those in set theory to include marginal, joint, posterior, conditional probabilities.
3. WHAT KIND OF MATERIALS ARE INCLUDED? The material included will consist of Microsoft PowerPoint slides accompanied my videos.
4. HOW LONG WILL THE COURSE TAKE TO COMPLETE? This course will take about one week to complete.
5. HOW WILL THE COURSE BE STRUCTURED? This course will be structured based on the book with the same title: A Manual for Strategic Economic Decision-Making: Using Bayesian Belief Networks to Make Complex Decisions (Grover, J) (Springer, 2016)
6.  WHY TAKE THE COURSE? This course is a primer for learning how to integrate discrete number sets and convert this information into business intelligence so that you can make complex decisions based on event driven economics.

KEY WORDS: Bayes theorem, Bayesian Statistics, Bayesian, Bayesian Network, Bayesian Analysis, Bayesian Belief Network, Bayesian Machine Learning, Inductive logic, conditional probability, posterior probability, joint probability, marginal probability.

Who is the target audience?
• Strategic Economic Decision Makers
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Curriculum For This Course
16 Lectures
32:02
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Introduction
2 Lectures 02:52

This is an introduction of the course and includes the topics that will be covered in the course.

Preview 02:48

These are the .png files from the Powerpoint presentation for this section.

Introduction-Slides
00:04
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Statistical Properties of Bayes' Theorem
4 Lectures 08:41
Preview 05:20

These are the .png files from the Powerpoint presentation for this section.

Part I Slides
00:07

Part II-MP4
03:09

These are the .png files from the Powerpoint presentation for this section.

Part II Slides
00:05
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Bayesian Research Protocol
2 Lectures 03:43
Preview 03:36

These are the .png files from the Powerpoint presentation for this section.

9 Step Bayesian Protocol-Slides
00:07
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Introduction to Base Matrix Protocol
2 Lectures 05:18

Students learn how the base matrices are created and then apply this learning to creating totally specified Bayesian Belief Networks in the remaining modules.

Preview 05:13

These are the .png files from the Powerpoint presentation for this section.

How to Create A, B1, C1, & D1 Base Matrices-Slides & Excel Spreadsheet
00:05
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2-Node Bayesian Belief Networks
2 Lectures 02:37
2-Node Bayesian Belief Network-MP4
02:32

These are the .png and Excel files from the Powerpoint presentation for this section.

2-Node Bayesian Belief Network-Slides & Excel Spreadsheet
00:05
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3-Node Bayesian Belief Networks
2 Lectures 03:24
3-Node Bayesian Belief Network-MP4
03:18

These are the .png files from the Powerpoint presentation for this section.

3-Node Bayesian Belief Networks-Slides & Excel Spreadsheet
00:06
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4-Node Bayesian Belief Network
2 Lectures 06:11
4-Node Bayesian Belief Network-MP4
06:04

These are the .png and Excel files from the Powerpoint presentation for this section.

3-Node Bayesian Belief Network-Slides & Excel Spreadsheet
00:07
 1.0 Average rating 2 Reviews 103 Students 3 Courses

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.