Probability methods for cost uncertainty analysis: a systems engineering perspective

Table of Contents:

SECTION 1: THEORY AND FOUNDATIONS

1. UNCERTAINTY AND THE ROLE OF PROBABILITY IN COST ANALYSIS

Introduction and Historical Perspective
The Problem Space
Presenting Cost as a Probability Distribution
Benefits of Cost Uncertainty Analysis

2. CONCEPTS OF PROBABILITY THEORY

Introduction
Sample Spaces and Events
Interpretations and Axioms of Probability
Conditional Probability
Bayes’ Rule

3. DISTRIBUTIONS AND THE THEORY OF EXPECTATION

Random Variables and Probability Distributions
Expectation of a Random Variable
Probability Inequalities Useful in Cost Analysis
Cost Analysis Perspective

4. SPECIAL DISTRIBUTIONS FOR COST UNCERTAINTY ANALYSIS

Trapezoidal Distribution
Beta Distribution
Normal Distribution
Lognormal Distribution
Specifying Continuous Probability Distributions

5. FUNCTIONS OF RANDOM VARIABLES AND THEIR APPLICATION TO COST UNCERTAINTY ANALYSIS

Introduction
Linear Combinations of Random Variables
Central Limit Theorem and a Cost Perspective
Transformations of Random Variables
Mellin Transform and Its Application to Cost Functions

6. SYSTEM COST UNCERTAINTY ANALYSIS

Work Breakdown Structures
Analytical Framework
Monte Carlo Simulation

7. MODELING COST AND SCHEDULE UNCERTAINTIES: AN APPLICATION OF JOINT PROBABILITY THEORY

Introduction
Joint Probability Models for Cost-Schedule
Summary


SECTION II: PRACTICAL CONSIDERATIONS AND APPLICATIONS


8. ELEMENTS OF COST UNCERTAINTY ANALYSIS: A REVIEW

Introduction
Cost as Probability Distribution
Monte Carlo Simulation and Method of Moments Distribution
Summary

9. CORRELATION: A CRITICAL CONSIDERATION

Introduction
Correlation Matters
Valuing Correlation
Summary

10. BUILDING STATISTICAL COST ESTIMATING MODELS

Introduction
Classical Statistical Regression
General Error Regression Method
Summary

11. MATHEMATICS OF COST IMPROVEMENT CURVES

Introduction
Learning Curve Theories
Production Cost Models Built by Single-Step Regression
Summary

12. ENHANCED SCENARIO-BASED METHOD

Introduction
Nonstatistical eSBM
Statistical eSBM
Historical Data for eSBM
Summary

13. COST UNCERTAINTY ANALYSIS PRACTICE POINTS

Treating Cost as a Random Variable
Risk versus Uncertainty
Subjective Probability Assessments
Subjectivity in Systems Engineering and Analysis Problems
Correlation
Capturing Cost-Schedule Uncertainties
Distribution Function of a System’s Total Cost
Benefits of Cost Uncertainty Analysis
Establishing a Cost and Schedule Risk Baseline
Determining Cost Reserve
Conducting Risk Reduction Trade-Off Analyses
Documenting the Cost Uncertainty Analysis
Management Perspectives

14. COLLECTED WORKS OF DR. STEPHEN A. BOOK

Textbooks
Journal Publications
Conference Presentations and Proceedings

Appendices

Index

Probability Methods for Cost Uncertainty Analysis: A Systems Engineering Perspective, Second Edition gives you a thorough grounding in the analytical methods needed for modeling and measuring uncertainty in the cost of engineering systems. This includes the treatment of correlation between the cost of system elements, how to present the analysis to decision-makers, and the use of bivariate probability distributions to capture joint interactions between a system’s cost and schedule. Analytical techniques from probability theory are stressed, along with the Monte Carlo simulation method. Numerous examples and case discussions illustrate the practical application of theoretical concepts.

While the original chapters from the first edition remain unchanged, this second edition contains new material focusing on the application of theory to problems encountered in practice. Highlights include the use of GERM to build development and production cost estimating relationships as well as the eSBM, which was developed from a need in the community to offer simplified analytical alternatives to advanced probability-based approaches. The book also lists the major technical works of the late Dr. Stephen A. Book, a mathematician and world-renowned cost analyst whose contributions advanced the theory and practice of cost risk analysis.


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