Stochastic reduced order models for random vectors: Application to random eigenvalue problems

► Improved algorithm for constructing stochastic reduced order models is proposed. ► The algorithm is shown to substantially increase model accuracy and efficiency. ► Analytical bounds are established for solutions to random eigenvalue problems. ► The models significantly outperform classic Monte Carlo in numerical examples.