A probabilistic framework for irreversible kinetics
A probabilistic framework is proposed to unify forward-in-time evolution and inverse inference for irreversible kinetics. It uses a cost functional J and a Gibbs measure, weighted by epistemic uncertainty Theta, to rank admissible histories as Bayesian posteriors. This approach reveals that enforcing causality in Generalized Standard Materials incurs an additional history-level cost compared to simultaneous minimization.