Stochastic simulation basically refers to Monte Carlo simulation methods. Thereby various variables and parameters of a system are scattered independently from each other according their probability distributions and then the effect of the resulting variables is described with the help of numeric simulation.
On the one side these methods can be used to describe and estimate probabilities and uncertainties. Especially for complex and nonlinear systems Monte Carlo methods are often the only pragmatic and practically feasible approach for the simulation based quantitative description of probabilities, uncertainties and risks.
On the other side the methods can be used for the identification and quantification of correlations and dependencies within complex systems with many variables and parameters.
The efficient exploratory investigation of complex systems is one of the main usecases for stochastic simulation beside robustness assessments.
A special competence of ANDATA is the usage of multi level stochastic simulations, which are helpful for conditional probabilities and Bayesian approaches.