Strategic economic decision-making [Documento electrónico] : using bayesian belief networks to solve complex problems / Jeff Grover
Language: eng.Country: US - United States of America.Publication: New York, NY : Springer , 2013Description: XI, 116 p. : il.ISBN: 978-1-4614-6040-4.Series: SpringerBriefs in StatisticsSubject - Topical Name: Teoria da decisão estatística bayesiana | Economia, Tomada de decisão Online Resources:Click here to access onlineItem type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | |
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E-Books | Biblioteca NOVA FCT Online | Não Ficção | QA279.5.SPR FCT 81417 (Browse shelf(Opens below)) | 1 | Available |
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QA279.5.SPR FCT 81126 Bayesian item response modeling, theory and applications | QA279.5.SPR FCT 81192 Frontiers of statistical decision making and bayesian analysis, in honor of James O. Berger | QA279.5.SPR FCT 81392 Applied bayesian statistics, with R and OpenBUGS examples | QA279.5.SPR FCT 81417 Strategic economic decision-making, using bayesian belief networks to solve complex problems | QA279.5.SPR FCT 81436 A multiple-testing approach to the multivariate Behrens-Fisher problem, with simulations and examples in SAS® | QA279.5.SPR FCT 81437 Bayesian networks in R, with applications in systems biology | QA279.5.SPR FCT 81543 Bayesian essentials with R |
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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 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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