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121205s2013 xxu| s |||| 0|eng d |
| 020 |
# |
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|a 9781461460404
|9 978-1-4614-6040-4
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| 024 |
7 |
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|a 10.1007/978-1-4614-6040-4
|2 doi
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| 050 |
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|a QA276-280
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| 072 |
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|a PBT
|2 bicssc
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| 072 |
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|a MAT029000
|2 bisacsh
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|a 519.5
|2 23
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| 100 |
1 |
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|a Grover, Jeff.
|e author.
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| 245 |
1 |
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|a Strategic Economic Decision-Making
|b Using Bayesian Belief Networks to Solve Complex Problems /
|c by Jeff Grover.
|h [electronic resource] :
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| 264 |
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1 |
|a New York, NY :
|b Springer New York :
|b Imprint: Springer,
|c 2013.
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| 300 |
# |
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|a XI, 116 p. 35 illus., 22 illus. in color.
|b online resource.
|
| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a SpringerBriefs in Statistics,
|v 9
|x 2191-544X ;
|
| 505 |
0 |
# |
|a 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.
|
| 520 |
# |
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|a 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.����
|
| 650 |
# |
0 |
|a Statistics.
|
| 650 |
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0 |
|a Mathematical statistics.
|
| 650 |
1 |
4 |
|a Statistics.
|
| 650 |
2 |
4 |
|a Statistics, general.
|
| 650 |
2 |
4 |
|a Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
|
| 650 |
2 |
4 |
|a Statistical Theory and Methods.
|
| 710 |
2 |
# |
|a SpringerLink (Online service)
|
| 773 |
0 |
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|t Springer eBooks
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| 776 |
0 |
8 |
|i Printed edition:
|z 9781461460398
|
| 830 |
# |
0 |
|a SpringerBriefs in Statistics,
|v 9
|x 2191-544X ;
|
| 856 |
4 |
0 |
|u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-4614-6040-4
|
| 912 |
# |
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|a ZDB-2-SMA
|
| 950 |
# |
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|a Mathematics and Statistics (Springer-11649)
|