Bayesian Posterior Analysis of 40 UAP Cases: Evaluating Framework Stability and Observational Uncertainty using JOR Fusion

This report presents a Bayesian posterior analysis of forty historical Unidentified Aerial Phenomena (UAP) cases using the James Orion Report (JOR) probabilistic framework. The operational JOR framework provides a structured methodology for evaluating UAP observations based on three primary scoring dimensions: witness credibility (C), environmental context (E), and physical sensor/radar evidence (P). Deterministic scoring produces baseline estimates for Solid Object Probability (SOP) and Non-Human Probability (NHP), while Bayesian sampling using PyMC assesses the stability of these estimates under observational uncertainty.

Posterior probability distributions were generated for each case to estimate posterior means and 95% credible intervals. The results indicate:

• Posterior means closely track deterministic NHP values across most cases.

• Credible intervals remain relatively narrow, showing stability even when input uncertainty is introduced.

• Cases with robust multi-sensor evidence exhibit the narrowest credible intervals, emphasizing the framework’s sensitivity to high-quality data.

• Bayesian weighting applies a conservative prior effect, preventing overconfident outliers.

• NHP remains anchored to SOP, ensuring anomalous classifications reflect actual evidentiary weight rather than isolated extreme kinematics.

The analysis demonstrates that the JOR Fusion implementation produces consistent probabilistic outcomes and provides a reproducible method for evaluating UAP observations.

This work complements the original JOR framework report (Orion, 2026, DOI: https://doi.org/10.5281/zenodo.18157347) and includes open, publicly archived code for independent replication and further analysis.

Author Note: This work was authored under the pseudonym James Orion. The legal name of the author is Jake James.