Comparative Bayesian Model Selection for Unidentified Anomalous Phenomena (UAP): A Multi-Modal Epistemic Evaluation of Origin Hypotheses

Abstract

Evaluating candidate hypotheses for Unidentified Anomalous Phenomena (UAP) presents a severe epistemic challenge characterized by sparse, multi-modal observational telemetry and high theoretical uncertainty. Competing hypotheses range from conventional sensor artifacts and classified aerospace assets to extraterrestrial technosignatures, cryptographic terrestrial biospheres, and higher-dimensional mechanisms. In this paper, we formulate a formal Bayesian model selection framework to quantitatively rank thirteen candidate origin hypotheses against authenticated physical and sensor telemetry. We define a multi-parameter Composite Data Quality Index (Q_j), construct an Occam-penalized Boltzmann prior distribution based on required theoretical degrees of freedom, and derive an exponential likelihood link function over eleven distinct empirical evidence vectors. Under nominal calibration (lambda = 4.5), the Extraterrestrial Hypothesis (H_ETH) achieves dominant posterior probability (P(H_ETH | E) = 0.997), yielding decisive Bayes factors over interdimensional (H_IDH), extratempestrial (H_TMP), and simulation (H_SIM) models, and overwhelming factors (B > 10^12) over conventional prosaic explanations (H_PRO). Sensitivity analyses across lambda in [2.0, 7.0], telemetry ablations, and uninformative flat priors demonstrate that this result is structurally robust, driven by the comparative physical parsimony of interstellar autonomous probes whose required performance can be framed within general-relativistic metric engineering and established exoplanetary demographics.