فهرست مطالب

• Preface
• I. Introduction and Background
• 1. An Analytical Framework
• A Decision-Making Framework
• The Analytics Evolution
• Uncertainty: Multiple Sources and Problems
• Probabilities and Uncertainty
• Prescriptive Analytics as a Separate Discipline
• The Analytics Flow
• Prescriptive Analytics and Decision Making
• Summary
• 2. Prescriptive Methods: Overview
• Introduction to Prescriptive Analytics Methods
• Summary of Prescriptive Analytics Methods
• Umbrella Classes: Non-Stochastic and Stochastic
• The Role of Operations Research
• Summary
• II. Essential Background Material
• 3. Python Essentials
• Python Structure: Overview
• Python Basics
• Conditional Statements: if-else
• Python Looping Constructs
• Python Packages
• Working with Python Packages
• Go-To References
• Summary
• 4. Probability Essentials
• The World Is Ruled by Probabilities
• What Are Probabilities?
• Fundamental Probability Concepts
• Limit Definition of Probabilities
• Subjective-Based Probabilities: Introduction
• Probability Distributions: Overview
• Summary
• III. Non-Stochastic Prescriptive Analytic Methods
• 5. Mathematical Programming: Overview
• Background
• Linear Programming
• Integer Programming
• Mixed Integer Programming
• Summary
• 6. Decision Tree Analysis: Overview
• Extending the Menu into Time
• Introduction to Decision Trees
• Clarification of Decision Trees
• Background
• Python Use-Case
• Reaching a Decision Using a DADT
• Summary
• DADT Function
• IV. Stochastic Prescriptive Analytic Methods
• 7. Simulation Essentials
• What Is a Simulation?
• Non-Stochastic Simulations: The Process
• Stochastic Simulations: The Process
• The Need for Stochastic Simulations
• Summary
• 8. Simulation Examples
• Example 1: Coin Toss
• Example 2: Die Toss
• Example 3: Regression Analysis
• Example 4: Mathematical Programming
• Example 5: Decision Tree
• Summary
• 9. Developing Menu Options
• The Nature of What-If Questions
• Menu Generating Questions: A Deep Dive
• Non-Stochastic Use-Cases
• Stochastic Use-Case: Synthetic Data
• Summary
• 10. Developing Menu Priors
• Background
• Digression on Beliefs and Priors
• Developing Probability Weights
• Eliciting Probability Distributions of Beliefs
• Analyzing Elicited Probability Distributions
• Python Use-Case
• Summary
• 11. One-Time Decisions
• Evidence of the Problem
• Sequential Decisions: Introduction
• Sequential Analysis: Advanced Framework
• Automating Sequential Decision Making
• Summary
• Glossary
• Bibliography
• Index
• About the Author