A Computational Framework for Detecting Numerical Patterns in Ancient Texts: Methods and Case Study

Abstract This study presents a computational framework addressing methodological challenges in numerical pattern analysis of ancient texts. Building on digital humanities best practices, we develop a discovery-validation-expert paradigm combining unsupervised anomaly detection, Bayesian model comparison, and cross-cultural validation with diachronic analysis. Applied to Hebrew biblical texts (focusing on Genesis) with Ugaritic and Akkadian control corpora, the framework includes structured expert validation. The methodology demonstrates four innovations: (1) separation of pattern discovery from hypothesis testing to prevent confirmation bias; (2) Bayesian multi-hypothesis comparison to quantify evidence strength; (3) diachronic stability analysis across manuscript traditions spanning two millennia; (4) systematic expert integration to formally anchor computational findings in scholarly interpretation. Our case study identifies statistically significant numerical correspondences (Bayes Factors > 8, providing strong statistical evidence) while modeling interpretive uncertainty through scenario-based analysis. This work establishes rigorous standards for computational analysis of cultural phenomena, offering objective tools for pattern detection and establishing a template for interoperable and transparent research practices in digital humanities. Keywords: Digital Humanities; Computational Philology; Bayesian Analysis; Ancient Texts; Cultural Analytics; Numerical Symbolism