Strategic Economic Decision Making

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Using Bayesian belief networks to solve complex problems.

97 students enrolled

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

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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About the Instructor

Solving Complex Problems, LINK by LINK.