The JOR Framework employs a dual-stage Bayesian pipeline that separates deterministic evidence structuring from probabilistic inference. The first stage transforms observational inputs into bounded evidentiary scores (SOP/NHP), while the second stage applies Bayesian updating to generate posterior estimates of non-human probability under uncertainty.
A baseline metric confirming physical reality before NHP (Non-Human Probability) analysis can commence.
Updates posteriors based on AARO-aligned standards (Prior = 0.20).
CALIBRATION (K): 0.20 // Prevents over-attribution under noisy sensor conditions.
Access the formal logic and operational protocols for the JOR-Fusion v3.1 framework.
The formal research report is distributed across these verified hubs.
VERSION HIERARCHY: v3.1 is the active PyMC inference engine. v3.0 is maintained as a baseline reference implementation for reproducibility and comparative validation. Both exist within the same JOR framework lineage.
JOR-FUSION-WEB (without PyMC)Witness (C): 0.40
Env (E): 0.30
Sensor (P): 0.30