The period centers on transforming uncertainty quantification through robust estimation, with formal robustness definitions, influence analysis, and practical algorithms designed for data with deviations. Bayesian and decision-theoretic approaches gain prominence, enabling integration of expert judgments, priors, and decision analyses across engineering and artificial intelligence. Information-theoretic and probabilistic foundations provide formal basis for maximum-entropy inference and combining information from diverse sources, while adaptive learning emphasizes learning the parameter vector during estimation and robustness in dynamic filtering. Influential Works: Information Theory and Statistical Mechanics introduced the maximum-entropy principle, offering a rigorous method to infer distributions from partial information and shaping probabilistic uncertainty quantification. Bootstrap Methods: Another Look at the Jackknife popularized resampling to estimate sampling distributions and construct confidence intervals, reshaping practical uncertainty assessment. The Influence Curve and its Role in Robust Estimation established the influence function as a core diagnostic for estimator robustness, guiding the development of robust procedures and adaptive methods; umbrella sampling and fuzzy sets broadened uncertainty modeling beyond classical probability, supporting reliable Monte Carlo quantification and alternative decision frameworks.