Assess students' ability to design, implement, and evaluate Bayesian inverse modeling frameworks that fuse atmospheric transport models and observational networks to quantify and attribute regional methane emissions for mitigation planning. Test skills in defining priors and likelihoods, specifying model and observation error covariances, selecting and configuring inversion algorithms (variational, ensemble, or MCMC), performing sensitivity and diagnostic tests (e.g., posterior diagnostics, footprint analysis, transport error assessment), interpreting posterior emissions and uncertainties, and deriving evidence-based mitigation priorities that account for observation coverage and transport-model biases.