Domain Contribution, Independence, and Correlation in the Unified Archetypal Bayesian Theory
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2025
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| _version_ | 1866901586951798784 |
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| author | Laing, Zachariah |
| author_facet | Laing, Zachariah |
| contents | <p>Anomalous Systems Analysis (ASA), as articulated across the Unified Archetypal Bayesian Theory (UABT) and its derivative frameworks, proposes that anomalous phenomena can be quantified through a multi-domain Bayesian structure uniting cognitive, cultural, environmental, and institutional data. As the theory has expanded through the Adaptive APC Forecast Engine (AAFE), the Southwest Verification Project (SWVP), and the Predictive Drift Observatory (PDO), researchers have increasingly recognized that the nine evidence domains (S₁–S₉) are not only unequally weighted in their contributions to posterior probability, but also show varying degrees of independence and correlation. This preprint examines the structure, behavior, and empirical performance of those domains through the lens of posterior contribution, domain independence, domain covariance, and correlation sensitivity—demonstrating how each element modifies the Bayesian coherence of UABT and reshapes future ASA research trajectories.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17642959 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Domain Contribution, Independence, and Correlation in the Unified Archetypal Bayesian Theory Laing, Zachariah Anthropology Anomalous Systems Analysis Unified Archetypal Bayesian Theory Bayesian statistics Data Science/statistics & numerical data <p>Anomalous Systems Analysis (ASA), as articulated across the Unified Archetypal Bayesian Theory (UABT) and its derivative frameworks, proposes that anomalous phenomena can be quantified through a multi-domain Bayesian structure uniting cognitive, cultural, environmental, and institutional data. As the theory has expanded through the Adaptive APC Forecast Engine (AAFE), the Southwest Verification Project (SWVP), and the Predictive Drift Observatory (PDO), researchers have increasingly recognized that the nine evidence domains (S₁–S₉) are not only unequally weighted in their contributions to posterior probability, but also show varying degrees of independence and correlation. This preprint examines the structure, behavior, and empirical performance of those domains through the lens of posterior contribution, domain independence, domain covariance, and correlation sensitivity—demonstrating how each element modifies the Bayesian coherence of UABT and reshapes future ASA research trajectories.</p> |
| title | Domain Contribution, Independence, and Correlation in the Unified Archetypal Bayesian Theory |
| topic | Anthropology Anomalous Systems Analysis Unified Archetypal Bayesian Theory Bayesian statistics Data Science/statistics & numerical data |
| url | https://doi.org/10.5281/zenodo.17642959 |