During this period, Bayesian reasoning coalesced into a formal framework for uncertainty, with explicit priors, likelihoods, and posterior representations guiding estimation and decision making. Researchers weighed subjective prior information against objective uncertainty, emphasizing principled prior elicitation and robustness through generalized or imprecise probability ideas. The program also linked Bayesian ideas to nonparametric intuition, using smoothing concepts and finite-population context to broaden applicability of Bayesian reasoning.