Bayesian Inference for Anticipating Systemic “Black Swan” Events via Cross-Domain Surprise Modeling

While Jinsheng Xie et al. (2010) use Bayesian surprise to flag unusual events in video, and Rahman et al. (2023) apply Bayesian inference to traffic anomalies, both remain domain-specific. This idea proposes a unified Bayesian model that learns to detect and anticipate “black swan” events by integrating models of Bayesian surprise from diverse domains—such as industrial faults (Yu et al., 2025), financial markets, and ecological systems (Han, 2025). The novelty is in cross-domain synthesis: by examining how anomalies propagate or co-occur across systems (e.g., a rare energy anomaly and an unexpected market event), the model could flag when a constellation of surprises signals a larger systemic risk. This approach could yield early warning systems for cascading failures—transformative for risk management, disaster prevention, and complex systems modeling.

References:

1. The detection of unusual events in video based on Bayesian surprise model. Jinsheng Xie, Li Guo, Yunbi Chen, Long Zhao (2010). International Conference on Information Science and Engineering.
2. A Variational Bayesian Inference-Based Robust Dissimilarity Analytics Model for Industrial Fault Detection. Wanke Yu, B. Huang, Gaoxi Xiao, Chuanke Zhang (2025). IEEE Transactions on Systems, Man, and Cybernetics: Systems.
3. Uncertainty-Aware Traffic Prediction using Attention-based Deep Hybrid Network with Bayesian Inference. M. Rahman, Abu Rafe Md Jamil, Naushin Nower (2023). International Journal of Advanced Computer Science and Applications.
4. Inter-Discipline Between Mathematical Modeling, Statistical Inference and Biology. Yuliang Han (2025). Theoretical and Natural Science.