Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizatio...
: The parity space approach to fault detection and isolation (FDI) has been developed during the last twenty years, and the focus here is to describe its application to stochastic ...
Probability distributions are useful for expressing the meanings of probabilistic languages, which support formal modeling of and reasoning about uncertainty. Probability distribu...
Bounded policy iteration is an approach to solving infinitehorizon POMDPs that represents policies as stochastic finitestate controllers and iteratively improves a controller by a...
Bayesian forecasting models provide distributional estimates for random parameters, and relative to classical schemes, have the advantage that they can rapidly capture changes in ...