A Comparison of Bayesian Methods for Integrated Test and Evaluation

Authors

Justin Krometis, Virginia Tech National Security InstituteFollow
Kyle Provost, Scientific Test and Analysis Techniques Center of Excellence, Air Force Institute of TechnologyFollow
Corinne Stafford, Scientific Test and Analysis Techniques Center of Excellence, Air Force Institute of TechnologyFollow
Victoria Sieck, Air Force Institute of TechnologyFollow
Laura Freeman, Virginia Tech National Security InstituteFollow

Document Type

Article

Publication Date

2025

Abstract

Testing defense systems in operationally realistic scenarios is typically logistically difficult and expensive. For this reason, Bayesian methods have gained significant interest in recent years as a means of shifting testing “left” in the acquisition lifecycle—that is, integrating information from earlier phases of test to reach conclusions about system performance more quickly and to better infer operational performance when data from such scenarios is limited. Bayesian inference mathematically quantifies assumptions in the form of selecting prior distributions on the unknown parameters and strategies for integrating data collected under different conditions. In this article, we compare several Bayesian approaches for integrated test and evaluation, using the example of estimating the reliability of the Stryker family of vehicles from developmental and operational test data. We compute posterior reliability estimates for each method and conduct a sensitivity analysis to measure how each assumption influences the results. Altogether, the analysis not only shows the promise of Bayesian integration of information, but also the importance of careful and justifiable assumptions to ensure defensible results.

Comments

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DOI

JSTOR:27425425.

Source Publication

Military Operations Research

Recommended Citation

Krometis, J., Provost, K., Stafford, C., Sieck, V., & Freeman, L. (2025). A Comparison of Bayesian Methods for Integrated Test and Evaluation. Military Operations Research, 30(3), 47–68.