A framework for assessing diagnostics model fidelity

"All models are wrong but some are useful" [1]. We address the problem of identifying which diagnosis models are more useful than others. Models are critical to diagnostics inference, yet little work exists to be able to compare models. We define the role of models in diagnostics inference, propose metrics for models, and apply these metrics to a tank benchmark system. Given the many approaches possible for model metrics, we argue that only information-theoretic methods address how well a model mimics real-world data. We focus on some well-known information-theoretic modelling metrics, demonstrating the trade-offs that can be made on different models for a tank benchmark system.