Probabilistic Churn Prediction with Bayesian Inference

Develop a sophisticated Python script using Bayesian probabilistic modeling to predict customer churn with high uncertainty quantification. Integrate multiple data sources including behavioral metrics, engagement scores, and historical churn patterns. Implement a PyMC3 or Stan-based probabilistic programming approach to model churn probability, generate credible intervals, and create a decision framework that accounts for both prediction and model uncertainty.