Strategic Economic Decision-Making

Using Bayesian Belief Networks to Solve Complex Problems

Nonfiction, Social & Cultural Studies, Social Science, Statistics, Science & Nature, Mathematics
Cover of the book Strategic Economic Decision-Making by Jeff Grover, Springer New York
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Author: Jeff Grover ISBN: 9781461460404
Publisher: Springer New York Publication: December 5, 2012
Imprint: Springer Language: English
Author: Jeff Grover
ISBN: 9781461460404
Publisher: Springer New York
Publication: December 5, 2012
Imprint: Springer
Language: English

Strategic Economic Decision-Making: Using Bayesian Belief Networks to Solve Complex Problems is a quick primer on the topic that introduces readers to the basic complexities and nuances associated with learning Bayes’ theory and inverse probability for the first time. This brief is meant for non-statisticians who are unfamiliar with Bayes’ theorem, walking them through the theoretical phases of set and sample set selection, the axioms of probability, probability theory as it pertains to Bayes’ theorem, and posterior probabilities. All of these concepts are explained as they appear in the methodology of fitting a Bayes’ model, and upon completion of the text readers will be able to mathematically determine posterior probabilities of multiple independent nodes across any system available for study.  Very little has been published in the area of discrete Bayes’ theory, and this brief will appeal to non-statisticians conducting research in the fields of engineering, computing, life sciences, and social sciences.    

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Strategic Economic Decision-Making: Using Bayesian Belief Networks to Solve Complex Problems is a quick primer on the topic that introduces readers to the basic complexities and nuances associated with learning Bayes’ theory and inverse probability for the first time. This brief is meant for non-statisticians who are unfamiliar with Bayes’ theorem, walking them through the theoretical phases of set and sample set selection, the axioms of probability, probability theory as it pertains to Bayes’ theorem, and posterior probabilities. All of these concepts are explained as they appear in the methodology of fitting a Bayes’ model, and upon completion of the text readers will be able to mathematically determine posterior probabilities of multiple independent nodes across any system available for study.  Very little has been published in the area of discrete Bayes’ theory, and this brief will appeal to non-statisticians conducting research in the fields of engineering, computing, life sciences, and social sciences.    

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