Noninferability
Noninferability is the property of a claim, attribute, or parameter that cannot be reliably inferred from available data or evidence within a given modeling framework. It denotes the opposite of inferability: even with access to data D and a specified model, the truth of a proposition H remains essentially unconstrained by D under the assumed assumptions.
In statistics and machine learning, noninferability arises when information about a parameter is vanishingly small or
In privacy and data publishing, noninferability characterizes the idea that released data should not allow reliable
Formal notions often use Bayesian or likelihood-based criteria. For a binary proposition H, noninferability can be