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Record Nr. |
UNINA9910438138503321 |
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Autore |
Grover Jeff |
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Titolo |
Strategic Economic Decision-Making [[electronic resource] ] : Using Bayesian Belief Networks to Solve Complex Problems / / by Jeff Grover |
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Pubbl/distr/stampa |
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New York, NY : , : Springer New York : , : Imprint : Springer, , 2013 |
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ISBN |
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1-283-93448-5 |
1-4614-6040-9 |
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Edizione |
[1st ed. 2013.] |
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Descrizione fisica |
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1 online resource (121 p.) |
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Collana |
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SpringerBriefs in Statistics, , 2191-544X ; ; 9 |
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Disciplina |
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Soggetti |
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Statistics |
Statistics, general |
Statistics for Social Sciences, Humanities, Law |
Statistical Theory and Methods |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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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