James Orion Report (JOR) Bayesian Fusion V2: Evidence-Driven SOP and NHP Analysis of UAP Cases

This preprint introduces a rigorous methodology for evaluating Unidentified Aerial Phenomena (UAP) by combining the James Orion Report (JOR) framework with Bayesian posterior analysis. The JOR framework quantifies Solid Object Probability (SOP) to establish whether a physical event occurred and Non-Human Probability (NHP) to assess anomalous characteristics, ensuring non-human hypotheses are evaluated only on a solid evidentiary foundation. This version includes: Formatting corrections for clarity and consistency. Addition of Limitations and Future Work, outlining constraints and potential extensions of the framework. Correction of the human-likelihood formula to: P(E|H) = min(1, 1 - NHP + K * SOP) ensuring that the likelihood of observing the evidence under a human-made hypothesis remains valid and conservative in Bayesian calculations. Using a stepwise workflow, the method incorporates witness credibility, environmental conditions, and physical/sensor evidence to generate weighted SOP and NHP scores. Bayesian reasoning then updates the likelihood of human versus non-human hypotheses, producing posterior probabilities that reflect both prior knowledge and observed evidence. Two illustrative cases — a Tier 1 UAP in Aguadilla, Puerto Rico (2013) and a Tier 2 UAP in Socorro, New Mexico (1964) — demonstrate the framework’s application, highlighting how evidence strength and prior probabilities influence posterior assessments. This approach enables reproducible, transparent, and systematic UAP analysis, offering a foundation for automated evaluation, rapid assessment of new cases, and integration with probabilistic programming tools for future research. Contact: Jake James Email: spaceydayz2@yahoo.com