We describe an architecture for representing and managing context shifts that supports dynamic data interpretation. This architecture utilizes two layers of learning and three layers of control for adapting and evolving new stochastic models to accurately represent changing and evolving situations. At the core of this architecture is a form of probabilistic logic used to encode sets of recursive relationships defining Dynamic Bayesian Models. This logic is extended with a syntax for defining contextual restrictions on stochastic Horn clauses. EM parameter learning is used to calibrate models as well as to assess the quality of fit between the model and the data. Model failure, detected as a poor fit between model and data, triggers a model repair mechanism based on causally informed context splitting and context merging. An implementation of this architecture for distributed weather monitoring is currently under development.