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Analyzing Risk through Probabilistic Modeling in Operations Research

Book Description

Probabilistic modeling represents a subject spanning many branches of mathematics, economics, and computer science to connect pure mathematics with applied sciences. Operational research also relies on this connection to enable the improvement of business functions and decision making. Analyzing Risk through Probabilistic Modeling in Operations Research is an authoritative reference publication discussing the various challenges in management and decision science. Featuring exhaustive coverage on a range of topics within operational research including, but not limited to, decision analysis, data mining, process modeling, probabilistic interpolation and extrapolation, and optimization methods, this book is an essential reference source for decision makers, academicians, researchers, advanced-level students, technology developers, and government officials interested in the implementation of probabilistic modeling in various business applications.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Editorial Advisory Board
  6. List of Reviewers
  7. Preface
  8. Acknowledgment
  9. Section 1: Probabilistic Modeling in Risk Analysis
    1. Chapter 1: Data Extrapolation via Curve Modeling in Analyzing Risk
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE METHOD OF HURWITZ-RADON MATRICES
      5. The Method of Hurwitz-Radon Matrices
      6. DISCUSSION OF EXAMPLES
      7. SOLUTIONS AND RECOMMENDATIONS
      8. FUTURE RESEARCH DIRECTIONS
      9. CONCLUSION
      10. REFERENCES
      11. ADDITIONAL READING
      12. KEY TERMS AND DEFINITIONS
    2. Chapter 2: The Method of Probabilistic Nodes Combination in Decision Making and Risk Analysis
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. NOVELTY OF PROBABILISTIC INTERPOLATION AND EXTRAPOLATION
      5. SOLUTIONS AND RECOMMENDATIONS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    3. Chapter 3: Perfect Partners of Mathematical Modeling with Technology in Risk Assessment
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE MODELING PROCESS
      5. ILLUSTRATIVE EXAMPLES: STARTING THE MODELING PROCESS
      6. ILLUSTRATIVE MODELING SOLUTIONS FOR EXAMPLES
      7. TECHNOLOGY
      8. CONCLUSION
      9. REFERENCES
    4. Chapter 4: Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. IMPORTANCE OF RISK PROFILING
      5. PARTICLE SWARM OPTIMIZATION ALGORITHM AND PROFILING FROM PREDICTIVE MODELS
      6. FUTURE RESEARCH DIRECTIONS
      7. DISCUSSION
      8. CONCLUSION
      9. REFERENCES
      10. ADDITIONAL READING
      11. KEY TERMS AND DEFINITIONS
    5. Chapter 5: A Bayesian Approach to Project Control
      1. ABSTRACT
      2. INTRODUCTION
      3. FORECASTING METHODOLOGIES
      4. KNOWLEDGE SOURCES
      5. FORECASTING BIAS
      6. EARNED VALUE MANAGEMENT SYSTEM
      7. BAYESIAN APPROACH
      8. APPLICATION
      9. CONCLUSION
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
    6. Chapter 6: A Sequential Probabilistic System for Bankruptcy Data Classification
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. METHODS
      5. DATA
      6. EXPERIMENTAL RESULTS
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
  10. Section 2: Decision Making and Risk Management
    1. Chapter 7: Risk Management
      1. ABSTRACT
      2. INTRODUCTION
      3. REVIEW OF RISK DEFINITIONS
      4. RISK CLASSIFICATIONS
      5. RISK MANAGEMENT METHOD
      6. RISK ANALYSIS AND EVALUATION: CASE STUDY
      7. BUSINESS CONTINUITY PLANS
      8. BUSINESS CONTINUITY MANAGEMENT: CASE STUDY
      9. SUMMARY
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
      12. ENDNOTES
    2. Chapter 8: A Different Way to Look at Random Variables
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS: FUNCTIONS OR SETS?
      5. SOLUTIONS AND RECOMMENDATIONS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
    3. Chapter 9: Simulation Output Analysis and Risk Management
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
      7. REFERENCES
      8. ADDITIONAL READING
      9. KEY TERMS AND DEFINITIONS
    4. Chapter 10: Costing Systems for Decision Making under Uncertainty Using Probabilistic Models
      1. ABSTRACT
      2. INTRODUCTION
      3. COSTING SYSTEMS AND COST ESTIMATION
      4. UNCERTAINTY HANDLING METHODS IN COSTING SYSTEMS
      5. PROPOSED MODEL
      6. APPLICATION
      7. CONCLUSION
      8. REFERENCES
    5. Chapter 11: Multi-Attribute Decision Making in Risk Analysis
      1. ABSTRACT
      2. INTRODUCTION
      3. DATA ENVELOPMENT ANALYSIS (DEA)
      4. SIMPLE ADDITIVE WEIGHTING (SAW) METHOD
      5. ANALYTICAL HIERARCHY PROCESS (AHP)
      6. TECHNIQUE OF ORDER PREFERENCE BY SIMILARITY TO THE IDEAL SOLUTION (TOPSIS)
      7. TOPSIS ILLUSTRATIVE EXAMPLES
      8. COMPARISON OF RESULTS FOR THE KITE NETWORK
      9. TECHNOLOGIES AVAILABLE
      10. FUTURE RESEARCH DIRECTION
      11. REFERENCES
  11. Section 3: Case Studies from Business and Industry
    1. Chapter 12: Condition-Based Maintenance
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. BASICS OF RELIABILITY THEORY
      5. DELAY TIME MODEL
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    2. Chapter 13: Applying Probabilistic Risk Assessment to Safety Risk Analysis in Aviation
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE INTEGRATED SAFETY ASSESSMENT MODEL
      5. RESULTS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    3. Chapter 14: Risk-Averse Newsboy Problem with Incomplete Demand Information
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. RISK-AVERSE NEWSBOY PROBLEM
      4. 3. RISK-AVERSE NEWSBOY PROBLEM WITH INCOMPLETE DEMAND INFORMATION
      5. 4. NUMERICAL ILLUSTRATION
      6. 5. CONCLUSION
      7. REFERENCES
    4. Chapter 15: Price Systems for Random Amounts
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. PRICE SYSTEMS: A GENERAL OVERLOOK
      5. SOLUTIONS AND RECOMMENDATIONS
      6. PRICE SYSTEMS IN MATHEMATICAL FINANCE
      7. RISK MEASURES
      8. INSURANCE PREMIA
      9. FURTHER RESEARCH DIRECTIONS
      10. CONCLUSION
      11. REFERENCES
      12. ADDITIONAL READING
      13. KEY TERMS AND DEFINITIONS
      14. APPENDIX: SUPER-HEDGING, ARBITRAGE AND INTERNALITY
    5. Chapter 16: Survival Analysis and ROC Analysis in Analyzing Credit Risks
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ANALAYZING CREDIT RISKS USING SURVIVAL MODELS
      5. EVALUATING CLASSIFICATION ACCURACY OF CREDIT SCORING SYSTEMS USING ROC ANALYSIS
      6. CONCLUSION
      7. FUTURE RESEARCH DIRECTIONS
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
  12. Compilation of References
  13. About the Contributors