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In the absence of (input) loading information, the identified modal properties from OMA often have significantly larger uncertainty (or variability) than their counterparts identified using free vibration or forced vibration (known input) tests. Ignoring this fact in the interpretation or presentation of identification results can lead to misrepresentation or over-confidence.
A [[Bayesian inference|Bayesian]] [[system identification]] approach is relevant for OMA as it provides a fundamental means for processing the information in the ambient vibration data for making inference on the modal properties in a manner consistent with [[probability]] logic and modeling assumptions. In addition to the most probable value, the identification uncertainty of the modal parameters can also be
The potential disadvantage of Bayesian approach is that the theoretical formulation can be more involved and less intuitive than their non-Bayesian counterparts. Algorithms are needed for efficient computation of the statistics (e.g., mean and variance) of the modal parameters from the [[posterior distribution]].
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