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Numerical Methods for Propogating Confidence Curves
Confidence curves—aka consonant confidence structures, aka inferential models—fuse the comprehensiveness and flexibility of Bayesian inference with the statistical performance and rigor of classical frequentist inference. Rooted in possibility theory, these structures visualize the long-known connection between confidence intervals and significance testing. More importantly, they enable the statistically reliable assignment of belief to propositions (or sets, hypotheses, etc.) about a fixed parameter being inferred from random data. This presentation explores a Monte Carlo approach to propagating these structures through black-box functions, a necessity if these methods are to be widely applied in engineering work.
Michael Balch is the Technical Lead at Alexandria Validation Consulting, LLC. He designs the algorithms underpinning our software and renders all consulting services personally. Dr. Balch has twelve years of experience as a research-practitioner specializing in uncertainty quantification. He has worked on applications spanning engineering, medicine, defense, and finance. His career has included time as a contractor at both NASA Langley and AFRL Wright-Patterson. He received his Doctorate in Aerospace Engineering from Virginia Tech in 2010.Further discussion with Michael Balch about his talk