Reconstructability
Reconstructability is the degree to which the original state, structure, or data of a system can be inferred from observed data, measurements, or partial information. It depends on identifiability (whether a unique reconstruction exists), the completeness and noise level of the observations, and the validity of the assumptions about the system. High reconstructability means a reconstruction mapping from observations to the original state is possible in a stable way; low reconstructability means multiple plausible reconstructions exist or small observation errors cause large changes in the inferred state.
In fields such as signal processing, imaging, statistics, and forensics, reconstructability governs what can be learned
Assessing reconstructability uses identifiability analysis, information-theoretic limits, and algorithmic reconstruction theory. Tools include rank conditions, convexity
Limitations include non-uniqueness, sensitivity to noise, model misspecification, data corruption, and computational complexity. Reconstructability is often
Overall, reconstructability quantifies the inferential potential of observed data and guides the design of data collection,