Distributionally robust Monte Carlo simulation: A tutorial survey

Constantino M. Lagoa, B. R. Barmish

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations


Whereas the use of traditional Monte Carlo simulation requires probability distributions for the uncertain parameters entering the system, distributionally robust Monte Carlo simulation does not. According to this new theory, instead of carrying out simulations using some rather arbitrary probability distribution such as Gaussian for the uncertain parameters, we provide a rather different prescription based on distributional robustness considerations. Motivated by manufacturing considerations, a class of distributions F is specified and the results of the simulation hold for all f ∈ F. This new method of Monte Carlo simulation was developed with the robustician in mind in that we begin only with bounds on the uncertain parameters and no a priori probability distribution is assumed.

Original languageEnglish (US)
Title of host publicationIFAC Proceedings Volumes (IFAC-PapersOnline)
EditorsGabriel Ferrate, Eduardo F. Camacho, Luis Basanez, Juan. A. de la Puente
PublisherIFAC Secretariat
Number of pages12
ISBN (Print)9783902661746
StatePublished - 2002
Event15th World Congress of the International Federation of Automatic Control, 2002 - Barcelona, Spain
Duration: Jul 21 2002Jul 26 2002

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
ISSN (Print)1474-6670


Other15th World Congress of the International Federation of Automatic Control, 2002

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering


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