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＜電子ブック＞
Strategic Economic Decision-Making : Using Bayesian Belief Networks to Solve Complex Problems / by Jeff Grover
(SpringerBriefs in Statistics. ISSN:21915458 ; 9)

版	1st ed. 2013.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2013
本文言語	英語
大きさ	XI, 116 p. 35 illus., 22 illus. in color : online resource
著者標目	*Grover, Jeff author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Social sciences -- Statistical methods  全ての件名で検索 FREE:Statistics FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy FREE:Statistical Theory and Methods
一般注記	Strategic Economic Decision Making: The Use of Bayesian Belief Networks (BBN) in Solving Complex Problems -- A Literature Review of Bayes’ Theorem and Bayesian Belief Networks (BBN) -- Statistical Properties of Bayes’ Theorem -- Bayes Belief Networks (BBN) Experimental Protocol -- Manufacturing Example -- Political Science Example -- Gambling Example -- Publicly Traded Company Default Example -- Insurance Risk Levels Example -- Acts of Terrorism Example -- Currency Wars Example -- College Entrance Exams Example -- Special Forces Assessment and Selection (SFAS) One-Stage Example -- Special Forces Assessment and Selection (SFAS) Two-Stage Example 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.     HTTP:URL=https://doi.org/10.1007/978-1-4614-6040-4

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