# Bayesian Smoothing **Entity class:** Time And Prediction Layer concept **Bayesian Smoothing** is a probabilistic construct for updating uncertainty about hidden states or competing explanations when evidence arrives. Bayesian Smoothing 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/Temporal Graph Analytics|Temporal Graph Analytics]]. - **ontology neighbors:** [[wiki/Bayesian Filtering|Bayesian Filtering]] and [[wiki/Online Inference|Online Inference]]. - **synthesis:** [[wiki/Counterterrorism Predictive Graph|Counterterrorism Predictive Graph]] and [[wiki/Predictive Intelligence Loop|Predictive Intelligence Loop]]. ## Sources - [IARPA — AGILE technical overview (accessed September 23, 2026)](https://www.iarpa.gov/images/research-programs/AGILE/AGILE_ModSim_-_Final.pdf) - [IARPA — Making Data Analysis More AGILE (accessed September 23, 2026)](https://www.iarpa.gov/newsroom/article/making-data-analysis-more-agile) **As of:** 2026-09-23