Learn to apply Bayesian belief networks for strategic economic decisions, using probability for assessing future outcomes in business contexts.
This course dives into Bayesian belief networks, helping folks understand how to use probabilities for smart business decisions. You’ll explore how to apply Bayes’ theorem and learn to model real-world problems with fun examples.
Using Bayesian belief networks to solve complex problems.
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.
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
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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...
Presentation was only as good as a PDF reader can make it. It can and should been made a lot more alive and fun to read/ listen to. Maybe even interactive.
I still feel there is a lot of extraneous content here. Trying to say too little with too many words.
Thank you.
The course was great march for my experience.
I'm very interested in the subject matter but the professor lacks teaching skills. You may as well just skip the recordings because he limits himself to reading straight from the slides, without expanding or adding to the material. The slides are plagued with typos and errors and the examples are taught at a theoretical level, with no emphasis on developing the intuition behind it.
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Saeed R.
There are so many obvious mistakes in the materials. It is unbelievable!!!!!!!!!!!!!!