Robust Bayesian Hierarchical Modeling Framework

Design a flexible Bayesian hierarchical modeling approach that can handle complex, nested data structures with sophisticated uncertainty quantification. Create a modular framework that can automatically adapt to different data distributions, manage partial pooling of information, and provide comprehensive probabilistic inference across multiple levels of aggregation. Include advanced techniques for model selection, prior specification, and posterior simulation.