LEADER 06574nam 22006135 450 001 9910155299303321 005 20200705180002.0 010 $a9783319484143 024 7 $a10.1007/978-3-319-48414-3 035 $a(CKB)3710000000964827 035 $a(DE-He213)978-3-319-48414-3 035 $a(MiAaPQ)EBC4751436 035 $a(PPN)197141439 035 $a(EXLCZ)993710000000964827 100 $a20161130d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe Manual of Strategic Economic Decision Making$b[electronic resource] $eUsing Bayesian Belief Networks to Solve Complex Problems /$fby Jeff Grover 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XXVIII, 260 p. 55 illus., 51 illus. in color.) 311 $a3-319-48413-3 311 $a3-319-48414-1 327 $a1. Introduction -- 1.1 Bayes' Theorem: An Introduction -- 1.2 Protocol -- 1.3 Data -- 1.4 Statistical Properties of Bayes' Theorem -- 1.5 Base Matrices -- 1.5.1 Event A Node -- 2. Base Matrices -- 2.1 Event A Node -- 2.1.1 Event A Node-Prior Counts -- 2.1.2 Module A-Prior Probabilities -- 2.2 Event B -- 2.2.1 Event B Node-Likelihood Counts -- 2.2.2 Module B Node -- 2.2.3 Event B Node-Counts -- 2.2.4 Event B Node-Likelihood Probabilities -- 2.3 Event C Node -- 2.3.1 Event C Node-Counts -- 2.3.2 Event C Node-Likelihood Probabilities -- 2.3.3 Event C Node-Counts -- 2.3.4 Event C Node-Likelihood Probabilities -- 2.3.5 Event C Node-Counts -- 2.3.6 Event C Node-Likelihood Probabilities -- 2.3.7 Event C Node-Counts -- 2.3.8 Event C Node-Probabilities -- 2.4 Event D Node -- 2.4.1 Event D Node-Counts -- 2.4.2 Event D Node-Likelihood Probabilities -- 2.5 Event D Node-Counts -- 2.5.1 Event D Node-Likelihood Probabilities -- 2.5.2 Event D Node-Counts -- 2.5.3 Event D Node-Likelihood Probabilities -- 2.5.4 Event D Node-Counts -- 2.5.5 Event D Node-Likelihood Probabilities -- 2.5.6 Event D Node-Counts -- 2.5.7 Event D Node-Likelihood Probabilities -- 2.5.8 Event D Node-Counts -- 2.5.9 Event D Node-Likelihood Probabilities -- 2.5.10 Event D Node-Counts -- 2.5.11 Event D Node-Likelihood Probabilities -- 3. 2-Event 1-Path BBN -- 3.1 [A] [B] -- 3.1.1 2-Event BBN Proof -- 3.1.2 BBN Specification -- 4.3-Event 2-Path BBNs -- 4.1 [AB|AC] -- 4.1.1 Proof -- 4.1.2 BBN Specification -- 4.2 [AC|BC] -- 4.2.1 Proof -- 4.2.2 BBN Specification -- 4.3 [AB|BC] -- 4.3.1 Proof -- 4.3.2 BBN Specification -- 5. 3-Event 3-Path BBNs -- 5.1 3-Paths-[AB|AC|BC] -- 5.1.1 Proof -- 5.1.2 BBN Probabilities. 330 $aThis book is an extension of the author?s first book and serves as a guide and manual on how to specify and compute 2-, 3-, & 4-Event Bayesian Belief Networks (BBN). It walks the learner through the steps of fitting and solving fifty BBN numerically, using mathematical proof. The author wrote this book primarily for naļve learners and professionals, with a proof-based academic rigor. The author's first book on this topic, a primer introducing learners to the basic complexities and nuances associated with learning Bayes? theory and inverse probability for the first time, was meant for non-statisticians unfamiliar with the theorem - as is this book. This new book expands upon that approach and is meant to be a prescriptive guide for building BBN and executive decision-making for students and professionals; intended so that decision-makers can invest their time and start using this inductive reasoning principle in their decision-making processes. It highlights the utility of an algorithm that served as the basis for the first book, and includes fifty 2-,3-, and 4-event BBN of numerous variants. Equips readers with a simplified reference source for all aspects of the discrete form of Bayes? theorem and its application to BBN Provides a compact resource for the statistical tools required to build a BBN Includes an accompanying statistical analysis portal Jeff Grover, PhD, is Founder and Chief Research Scientist at Grover Group, Inc., where he specializes in Bayes? Theorem and its application to strategic economic decision making through Bayesian belief networks (BBNs). He specializes in blending economic theory and BBN to maximize stakeholder wealth. He is a winner of the Kentucky Innovation Award (2015) for the application of his proprietary BBN big data algorithm. He has operationalized BBN in the healthcare industry, evaluating the Medicare ?Hospital Compare? data; in the Department of Defense, conducting research with U.S. Army Recruiting Command to determine optimal levels of required recruiters for recruiting niche market medical professionals; and in the agriculture industry in optimal soybean selection. In the area of economics, he was recently contracted by the Department of Energy, The Alliance for Sustainable Energy, LLC Management and Operating Contractor for the National Renewable Energy Laboratory, to conduct a 3rd party evaluation of the Hydrogen Financial Analysis Scenario (H2FAST) Tool. 606 $aStatistics  606 $aEconometrics 606 $aOperations research 606 $aDecision making 606 $aManagement 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010 606 $aEconometrics$3https://scigraph.springernature.com/ontologies/product-market-codes/W29010 606 $aOperations Research/Decision Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/521000 606 $aManagement$3https://scigraph.springernature.com/ontologies/product-market-codes/515000 615 0$aStatistics . 615 0$aEconometrics. 615 0$aOperations research. 615 0$aDecision making. 615 0$aManagement. 615 14$aStatistical Theory and Methods. 615 24$aStatistics for Business, Management, Economics, Finance, Insurance. 615 24$aEconometrics. 615 24$aOperations Research/Decision Theory. 615 24$aManagement. 676 $a519.5 700 $aGrover$b Jeff$4aut$4http://id.loc.gov/vocabulary/relators/aut$0756110 906 $aBOOK 912 $a9910155299303321 996 $aThe Manual of Strategic Economic Decision Making$92093310 997 $aUNINA