Many theoretical and numerical advances have recently been realized in the field of stochastic optimization.
Approximation algorithms are the prevalent solution methods in the field of stochastic programming.
Optimal stopping problems of stochastic systems play an important role in the field of stochastic control theory.
This article presents selected research results in the field of stochastic dynamic stability problems.
The well-posedness of FBSDEs is also hard to get, and it has widely practical applications in the field of stochastic optimal control as well as financial mathematics.
Also in the field of stochastic signal processing (SP) and Bayesian state estimation, high-dimensional problems become more and more relevant.
Various application examples illustrate the approach, both from the field of global SA (i.e. well-known benchmark problems) and from the field of stochastic mechanics.
They were successfully applied to solve stationary problems e.g. in the field of stochastic finite elements [10] and to solve differential equations with uncertain parameters and initial values [12].
The Stand Alone Tool Box covers a fairly wide field of stochastic methods including various sampling techniques, random fields, fatigue analysis, reliability based optimization, random vibration, Monte Carlo simulation and FE-analysis.
This paper surveys the theoretical developments in the field of stochastic process algebras, process algebras where action occurrences may be subject to a delay that is determined by a random variable.
