LEADER 04208nam 22006735 450 001 9910438138503321 005 20200703151858.0 010 $a1-283-93448-5 010 $a1-4614-6040-9 024 7 $a10.1007/978-1-4614-6040-4 035 $a(CKB)3400000000093772 035 $a(EBL)1082067 035 $a(OCoLC)822978250 035 $a(SSID)ssj0000811904 035 $a(PQKBManifestationID)11432708 035 $a(PQKBTitleCode)TC0000811904 035 $a(PQKBWorkID)10859116 035 $a(PQKB)11541955 035 $a(DE-He213)978-1-4614-6040-4 035 $a(MiAaPQ)EBC1082067 035 $a(PPN)168304562 035 $a(EXLCZ)993400000000093772 100 $a20121205d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStrategic Economic Decision-Making$b[electronic resource] $eUsing Bayesian Belief Networks to Solve Complex Problems /$fby Jeff Grover 205 $a1st ed. 2013. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2013. 215 $a1 online resource (121 p.) 225 1 $aSpringerBriefs in Statistics,$x2191-544X ;$v9 300 $aDescription based upon print version of record. 311 $a1-4614-6039-5 320 $aIncludes bibliographical references and index. 327 $aStrategic 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. 330 $aStrategic Economic Decision-Making: Using Bayesian Belief Networks to Solve Complex Problems is a quick primer on the topic that introduces readers to the basic complexities and nuances associated with learning Bayes? theory and inverse probability for the first time. This brief is meant for non-statisticians who are unfamiliar with Bayes? theorem, walking them through the theoretical phases of set and sample set selection, the axioms of probability, probability theory as it pertains to Bayes? theorem, and posterior probabilities. All of these concepts are explained as they appear in the methodology of fitting a Bayes? model, and upon completion of the text readers will be able to mathematically determine posterior probabilities of multiple independent nodes across any system available for study.  Very little has been published in the area of discrete Bayes? theory, and this brief will appeal to non-statisticians conducting research in the fields of engineering, computing, life sciences, and social sciences.    . 410 0$aSpringerBriefs in Statistics,$x2191-544X ;$v9 606 $aStatistics  606 $aStatistics, general$3https://scigraph.springernature.com/ontologies/product-market-codes/S0000X 606 $aStatistics for Social Sciences, Humanities, Law$3https://scigraph.springernature.com/ontologies/product-market-codes/S17040 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 610 4$aStatistics. 610 4$aMathematical statistics. 610 4$aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law. 610 4$aStatistical Theory and Methods. 615 0$aStatistics . 615 14$aStatistics, general. 615 24$aStatistics for Social Sciences, Humanities, Law. 615 24$aStatistical Theory and Methods. 676 $a519.5 676 $a519.5/42 676 $a519.542 700 $aGrover$b Jeff$4aut$4http://id.loc.gov/vocabulary/relators/aut$0756110 906 $aBOOK 912 $a9910438138503321 996 $aStrategic Economic Decision-Making$92502693 997 $aUNINA