Manual of Strategic Economic Decision Making

Using Bayesian Belief Networks to Solve Complex Problems

Häftad, Engelska, 2018

AvJeff Grover

1 039 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

Finns i fler format (1)

Inbunden

1 069 kr

Skickas inom 10-15 vardagar

This 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-, and 4-Event Bayesian Belief Networks (BBN).

Produktinformation

Utgivningsdatum2018-04-29
Mått155 x 235 x 16 mm
Vikt441 g
FormatHäftad
SpråkEngelska
Antal sidor260
FörlagSpringer International Publishing AG
ISBN9783319839370

Tillhör följande kategorier

Matematisk statistik inom Naturvetenskap och teknik
Företagsekonomi inom Ekonomi och Ledarskap
Nationalekonomi inom Ekonomi och Ledarskap
Mikroekonomi inom Ekonomi och Ledarskap

Jeff Grover, Doctor of Business Administration (DBA) (Finance), 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 (BBN). 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.

1. 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.