Strategic Economic Decision Making | Social Science Online Course by Udemy

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Using Bayesian belief networks to solve complex problems.

An excellent training about Social Science

Strategic Economic Decision Making

Grover Group, Inc. (GGI), offers this course so that learners canuse inductive logic when makingbusiness 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). Thiscourse is a thorough investigation on Bayesian belief networks (BBN), where we will provide the learner with the underlying principlesassociated 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 thosethat have a basic understanding of statisticsandBayes’ theorem. During this course, we will walk the learnerthrough 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 usingset theory and discreteaxioms of probability, These underlying concepts include counting and subsequent calculationofprior, 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 offitting a Bayes’ model in this course. Upon course completion, the learner canmathematically 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.

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