# Bayesian Filtering **Entity class:** Time And Prediction Layer concept **Bayesian Filtering** is a probabilistic construct for updating uncertainty about hidden states or competing explanations when evidence arrives. The operational value of Bayesian Filtering lies in preserving time, provenance, confidence, and alternative explanations. ## 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/Particle Filter|Particle Filter]] and [[wiki/Bayesian Smoothing|Bayesian Smoothing]]. - **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