Strategic Economic Decision-Making : Using Bayesian Belief Networks to Solve Complex Problems
Language: English
Published by Springer New York, 2012
- Softcover
- Used
Condition: Used - Fine
Current price: £ 37.05
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Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | 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.…
Seller Inventory # 23038736/12
- Title
- Strategic Economic Decision-Making : Using Bayesian Belief Networks to Solve Complex Problems
- Author
- Jeff Grover
- Publisher
- Springer New York
- Publication year
- 2012
- Condition
- Sehr gut
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1461460395
- ISBN 13
- 9781461460398
- Seller catalogs
- Bücher
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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