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This skill empowers developers and data scientists to implement robust Bayesian statistical models, including hierarchical structures, time series analysis, and generalized linear models. It provides a standardized workflow for the entire modeling lifecycle—from data preparation and prior predictive checks to MCMC sampling with NUTS, rigorous diagnostic evaluation, and posterior predictive validation. By integrating best practices for parameterization and model comparison, it ensures high-quality inference and reliable uncertainty quantification for data-driven decision making.
