JOR-Bayesian Fusion Framework (v3.1)

Probabilistic UAP Analysis Engine

📌 DOI: https://doi.org/10.5281/zenodo.18157347
📦 Zenodo Record: https://zenodo.org/records/18157347

The James Orion Report (JOR) Bayesian Fusion framework is a probabilistic analysis system designed to evaluate Unidentified Anomalous Phenomena (UAP) using structured evidentiary scoring combined with Markov Chain Monte Carlo (MCMC) sampling via PyMC.

The system integrates qualitative observation rubrics with quantitative Bayesian-style inference to produce conservative estimates of whether a case is consistent with a non-human origin hypothesis.

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🚀 Key Improvements in v3.1

• Unified Parameter Sync: Global constants (K-calibration, priors, and weights) are centrally defined in jor_fusion.py and inherited across all execution layers, ensuring consistency between CLI scoring and Bayesian inference.
• Evidence-Layer Flight Modulation: Flight-related anomalies are now treated as a probabilistic modifier on physical evidence quality rather than a post-hoc score adjustment. This ensures anomaly information is incorporated at the evidentiary interpretation stage.
• Numerical Stability Improvements: Variable clipping (0.001–0.999) prevents invalid probability states, and fixed random seeds ensure reproducible MCMC sampling.
• Dynamic Scorer Logic: The interactive CLI includes conditional scoring adjustments for high-confidence observational conditions (e.g., clear daytime visibility), reducing under-weighting of high-quality data.

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📂 Repository Structure

File	Description
Full Methodology Report	Original theoretical framework and evidentiary rubric definitions
jor_fusion.py	Scorer: Interactive CLI tool for structured case input and deterministic scoring.
jor_pymc.py	Bayesian Engine: PyMC-based probabilistic model for posterior estimation via MCMC sampling.
jor_pymc_runner.py	Batch Orchestrator: Executes multiple case evaluations and generates confidence intervals.
jor_scores.csv	Outputs: Model-generated posterior estimates, means, and credible intervals for evaluated cases.

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📊 Example Dataset

A curated dataset of 50 evaluated UAP cases using the v3.1 framework is included:

• data/v3_1/jor_uap_cases_50_v3_1.csv

This dataset contains structured inputs and model outputs, including:

• Evidence scores (C, E, P)
• Flight anomaly modifiers
• SOP / NHP intermediate values
• Posterior probabilities and credible intervals

It can be used to:

• Reproduce published results
• Validate model behavior
• Test parameter sensitivity and calibration

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🧭 Version Philosophy (v3 vs v3.1)

• v3: Additive feature-weight scoring model where anomaly indicators influence posterior estimates through weighted contributions.
• v3.1: Evidence-conditioned inference model where observational reliability (e.g., flight anomalies) modifies how physical evidence is interpreted within the likelihood structure.

This distinction is critical: v3.1 does not simply re-weight outputs—it modifies how evidence is conditioned before probabilistic inference.

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🧪 The Bayesian Logic

The framework evaluates competing hypotheses:

• H (Human/Prosaic Origin)
• NH (Non-Human / Anomalous Origin)

Priors are initialized as:

• P(H) = 0.80
• P(NH) = 0.20

These priors represent conservative baseline assumptions under conditions of uncertainty.

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Stochastic Flight Modeling

A key component of the model is the Flight Effect, which introduces a probabilistic modulation of physical evidence strength based on observed kinematic anomalies and assumed measurement uncertainty.

This adjustment is applied at the evidence interpretation stage:

$$ P_{Anomalous} = \text{clip}(P \times (1 + \text{Flight Effect}), 0.0, 0.95) $$

Where the Flight Effect is modeled as a truncated distribution:

• TruncatedNormal(μ = mod, σ = 0.03, lower = 0.0, upper = 0.10)

This formulation ensures that anomalous flight characteristics contribute proportionally to evidence strength while remaining bounded by observational uncertainty.

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K-Calibration Constant (Heuristic Likelihood Proxy)

To maintain conservative inference behavior aligned with uncertainty constraints, the framework includes a calibration constant:

$$ P(E|H) = \min(1, 1 - NHP + K \cdot SOP) $$

Where:

• K = 0.20 (calibration constant)
• SOP = Solid Object Probability
• NHP = Non-Human Probability (intermediate estimate)

This expression is a heuristic likelihood proxy, not a strict probabilistic identity. It is used to stabilize inference under incomplete observational data.

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🛠️ Usage

Requirements and Execution

Install dependencies and run the framework:

pip install pymc pytensor pandas numpy colorama
python jor_fusion.py
python jor_pymc_runner.py