Probabilistic Mathematical-Legal Analysis of the Epstein Case Using Bayesian and Psychological Evidence Models

The death of Jeffrey Epstein in August 2019 generated widespread public debate due to (i) the magnitude of his crimes, (ii) the institutional failures surrounding his detention, and (iii) the emergence of alternative hypotheses including murder or orchestrated escape. This study applies a mathematical-legal probabilistic framework, combining Bayesian inference, psychological analysis, and evidence-weighted legal modelling, to evaluate the plausibility of competing hypotheses: H₁: suicide, H₂: homicide, H₃: staged escape (faked death). A simple Bayesian model is demonstrated with real executable code, showing that under current public evidence, P(H₁|E) ≈ 0.994, while P(H₂|E) ≈ 0.006, and P(H₃|E) is too small to compute meaningfully due to lack of supporting evidence. The analysis concludes that the official suicide explanation remains most consistent with available evidence, though psychological and sociological dynamics explain why low-probability alternatives remain culturally persistent. Language editing assistance was provided using an AI-based tool. Originality and AI-use statement: This work is an original research output by Begüm Yıldırım. AI tools, if used, were limited to language refinement, grammar correction, formatting, translation assistance, and clarity improvement. The conceptual framework, research direction, interpretation, models, and conclusions belong to the author. External sources, datasets, or prior works are cited where applicable.