# Identifiability **Entity class:** Probabilistic And Inverse-Problem Layer concept **Identifiability** is a concept in the probabilistic and inverse-problem of the counterterrorism predictive-graph ontology. Identifiability belongs in the ontology because AI can compute over this structure while analysts retain responsibility for interpretation and authorized action. ## Counterterrorism predictive-graph role The page records capability and analytic function. Any claim of a named operational deployment remains subject to the [[wiki/Counterterrorism Predictive Graph Evidence Ladder|evidence ladder]]. The analytic state should distinguish ground truth, observed evidence, inferred state, and predicted state. ## Relationships - **domain router:** [[wiki/Bayesian Inference|Bayesian Inference]]. - **ontology neighbors:** [[wiki/Sensitivity Analysis|Sensitivity Analysis]] and [[wiki/Ill-Posed Problem|Ill-Posed Problem]]. - **synthesis:** [[wiki/Counterterrorism Predictive Graph|Counterterrorism Predictive Graph]] and [[wiki/Predictive Intelligence Loop|Predictive Intelligence Loop]]. ## Sources - [NIST/SEMATECH — e-Handbook of Statistical Methods (accessed September 23, 2026)](https://www.itl.nist.gov/div898/handbook/) - [Oden Institute — Tan Bui-Thanh research profile (accessed September 23, 2026)](https://oden.utexas.edu/people/directory/Tan-Bui/) **As of:** 2026-09-23