James Orion Report (JOR) Bayesian Fusion: Evidence-Driven SOP and NHP Analysis of UAP Cases
Authors/Creators
Description
Title:
James Orion Report (JOR) Bayesian Fusion: Evidence-Driven SOP and NHP Analysis of UAP Cases
Authors:
Jake James (James Orion)
Executive Summary:
The JOR Bayesian Fusion Framework is a civilian-led operational UAP research framework designed for evidence-based triage and multi-source data fusion. The James Orion Report (JOR v3) defines a Structured Probabilistic Triage Framework (SPTF) using Bayesian fusion, providing a reproducible and transparent method for evidence-driven UAP case analysis. It establishes a standardized, probabilistic methodology to prioritize Unidentified Anomalous Phenomena (UAP) reports for scientific and governmental evaluation.
Description / Abstract:
Purpose:
A practical evidentiary triage framework for separating credible solid-object observations from speculative non-human interpretations in safety-critical observational and reporting contexts.
Design:
Modular and system-agnostic, intended for integration with existing sensor fusion pipelines, analytic workflows, and decision-support systems.
Scope:
Applicable to aviation and aerospace safety, defense and intelligence reporting, scientific anomaly review, and other environments where evidentiary discipline is required prior to higher-order interpretation.
This preprint presents JOR Framework v3, a rigorous methodology for evaluating Unidentified Aerial Phenomena (UAP) using Bayesian posterior analysis. This Bayesian evidence fusion framework combines the James Orion Report (JOR) system with probabilistic reasoning to provide structured, evidence-driven analysis.
The methodology quantifies:
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Solid Object Probability (SOP): the likelihood that a physical event occurred
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Non-Human Probability (NHP): an anomaly-weighted score indicating deviation from conventional human or natural explanations
By integrating witness credibility, environmental conditions, and physical/sensor evidence, the framework produces weighted SOP and NHP scores. Bayesian updating then calculates posterior probabilities, reflecting both prior knowledge and observed evidence. This ensures that non-human hypotheses are evaluated only on a solid evidentiary foundation.
v3 updates: formatting corrections, added Limitations and Future Work section, and corrected human-likelihood formula.
Two illustrative cases demonstrate practical application:
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Tier 1 UAP — Aguadilla, Puerto Rico (2013)
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Tier 2 UAP — Socorro, New Mexico (1964)
JOR Framework v3 supports reproducible, transparent, and systematic analysis, enabling automated evaluation, rapid assessment of new cases, and integration with probabilistic programming tools for future research.
Clarification:
The JOR framework is a triage-oriented probabilistic system, designed to work under conditions of incomplete data and information asymmetry. Several design choices—like using bounded heuristic fusion operators, agency-specific parameterization, and qualitative scoring rubrics—are intentional constraints, not unresolved limitations. Sensitivity analyses show robustness to changes in priors and parameters, and the framework supports transparent calibration when institutional data is available. These features make the framework both auditable and adaptable across different operational contexts, while keeping triage decisions interpretable and defensible. This isn’t meant to be a final attribution system—rather, it’s a tool to help prioritize cases for deeper analysis.
Keywords:
UAP, Bayesian Fusion, SOP, NHP, probabilistic framework, decision support, evidence-driven analysis
Version:
v3
Related Works:
JOR Framework v3.1: Organizational User Manual — Bayesian Fusion Engine with Stochastic Flight Modeling for UAP Case Triage
https://doi.org/10.5281/zenodo.19688346
JOR Framework v3: Organizational User Manual — Field Guidance for Data-Driven UAP Case Triage
https://doi.org/10.5281/zenodo.18203566
https://github.com/jamesorion6869/JOR_PYMC_V3_1
Contact / Email:
Jake James (James Orion)
spaceydayz2@yahoo.com
Programming Language:
Python
Abstract (English)
Status: Project Complete — v3.1 Final Release
This update reflects the implementation of the JOR Bayesian Fusion framework using the PyMC probabilistic programming library.
The prior probabilities are based on contemporary UAP reporting and are deliberately conservative. Alternative priors or weighting schemes can be substituted without altering the underlying structure, allowing for sensitivity testing and comparative analysis across assumptions.
Following the initial conceptual formulation, the core Bayesian updating loop has been implemented and automated using PyMC in Python. This transitions the framework from manual illustrative calculations to continuous random variables and MCMC-based sampling.
While case ingestion remains manually scored to preserve its role as a structured decision-support layer, the Python implementation establishes a scalable probabilistic architecture capable of handling larger pre-scored UAP datasets, enabling rapid sensitivity analysis and reducing calculation error.
The repository further extends outputs into operational aviation safety metrics via a risk-binning architecture, including flight hazard and collision risk components. The open-source implementation and supporting modules are available in the project repository.
Files
JOR_Bayesian_Fusion_(V3.1).pdf
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Additional details
Related works
- Is supplemented by
- Publication: 10.5281/zenodo.19688346 (DOI)
- Other: https://jamesorion6869.github.io/jor-fusion-web/reference.html (URL)
Software
- Repository URL
- https://github.com/jamesorion6869/JOR_PYMC_V3_1
- Programming language
- Python
- Development Status
- Active