Evaluate graduate-level ability to design, implement, and interpret Bayesian network models that integrate environmental (e.g., precipitation, topography, drainage, land cover) and socioeconomic (e.g., population density, income, infrastructure vulnerability) datasets for urban flood risk assessment; tasks include variable selection and causal structure specification, parameter learning with heterogeneous and missing data, spatiotemporal inference and uncertainty quantification, sensitivity analysis, model validation against observed flood events, and translating results into policy or mitigation recommendations.