Confidence intervals (CIs) are common methods to characterize the uncertain output of experimental measurements, process design calculations and simulations. Usually, probability distributions (pdfs) such as Gaussian and t-Student are used to quantify them. There are situationswhere the pdfs have anomalous behavior such as heavy tails, which can arise in uncertainty analysis of nonlinear comput...

We analyze dynamic allocations in a model with uncertain demand and with unobservable arrivals. The planner learns along the way about future demand, but strategic agents, who anticipate this behavior, strategically choose the timing of their arrivals. We examine the conditions under which the complete information, dynamically efficient allocation is implementable, and characterize the necessar...

A numerical algorithm for computing necessary conditions for performance speciications is developed for nonlinear uncertain systems. The algorithm is similar in nature and behavior to the power algorithm for the lower bound, and doesn't rely on a descent method. The algorithm is applied to a practical example.

PEComas are uncommon mesenchymal tumors often involving the pelvic organs. They have an unpredictable behavior. Accurate diagnosis and long-term follow-up is therefore essential in these patients. We report this case of PEComa of uncertain malignant potential in an unusual location with excellent prognosis.