Learn Bayes' Theorem Proofs
- 40 mins on-demand video
- 6 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- Learn how to perform a mathematical proof of Bayes' theorem.
- Download PowerPoints to follow through with the videos.
This lesson is an excerpt from the published, Strategic Economic Decision-Making: Using Bayesian Belief Networks to Solve Complex Problems (Grover 2013). I have realized that during the literature review how much industry has greatly benefited from the utility of Bayesian Belief Networks (BBN). In fact, my latest Google search using the keyword "Bayesian" populated more than 12,000,000 hits. When I wrote this book, this search term had 4,000,000 hits. I am predicting exponential interest in Bayes' theorem and BBN in the next decade. Now, we have high performance computers and enormous amount of data that is being collected globally by governments and private sector industries, especially Twitter, Facebook, and Google, which is mind-boggling. My goal is to provide the learner with simple proofs to use and reference to learn how to convert this data information into business intelligence and BBN are proving to be the tool of choice in this endeavor. A limitation to learning BBN is that the statistical and computer programming symbology are not straight forward for the naive student to read and comprehend. My concern is that this eloquent concept is not transparent to the human consumer of decision-making processes. The use of Bayes' theorem and inductive logic allows for the embedding of subjective matter expertise as a starting point for executive decision-making and is an indispensable tool in decision theory.
The scope of this course is to explore the constructs of statistical proofs of two, three, and four event Bayesian Belief Networks (BBN). The target audience are those learners who are interested in the mathematical underpinnings of BBN.
- This course is about the utility of Bayesian proofs that lead to the formulation of BBN.
- The terminology is basic statistical notation as referenced in basic discrete math application.
- The materials include in this course are step-by-step videos with accompanying PowerPoint presentations that complement the videos.
- This course should take 5 hours to complete.
- This course is structured as a step-by-step approach to learning mathematical proofs.
- Learners should take this course to increase their understanding of BBN.
- This proof series is meant for learners who are not familiar with Bayes' theorem who are looking for the basis of Bayesian Belief Networks. No prior knowledge is proof solving is required.