Bayesian Network Modeling for Probabilistic Reasoning and Risk Assessment in Large-Scale Industrial Datasets

In complex industrial environments, uncertainty is inherent in decision-making due to dynamic operating conditions, sensor variability, and the vast heterogeneity of data sources. Traditional deterministic models often fall short in capturing the probabilistic dependencies and hidden causal relationships that characterize these systems. As industries increasingly adopt data-driven strategies, probabilistic reasoning frameworks such as Bayesian Network (BN) modeling have gained prominence for their ability to encode domain knowledge, handle incomplete information, and support transparent inference under uncertainty. Bayesian Networks offer a graphical model-based approach to representing joint probability distributions over multiple interrelated variables. In large-scale industrial datasets—ranging from manufacturing process logs and predictive maintenance records to energy grid telemetry and supply chain metrics—BNs enable efficient reasoning by decomposing complex dependencies into directed acyclic graphs. These structures support not only diagnostic and prognostic tasks but also counterfactual analysis and real-time decision support. This paper explores the methodology and practical application of Bayesian Network Modeling for probabilistic reasoning and risk assessment in industrial contexts. Emphasis is placed on model construction from big data, structure learning from high-dimensional variables, and parameter estimation under noisy or partially missing data. Case studies from fault prediction in chemical processing plants and anomaly detection in smart grid infrastructure illustrate the scalability and interpretability of BNs in practice. The integration of expert knowledge with data-driven inference highlights the hybrid power of Bayesian models in enhancing industrial resilience, safety, and strategic planning.