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dc.contributor.authorQuartuccio, John
dc.contributor.authorGiammarco, Kristin
dc.contributor.authorAuguston, Mikhail
dc.date.accessioned2018-07-23T19:19:08Z
dc.date.available2018-07-23T19:19:08Z
dc.date.issued2017-07-31
dc.identifier.citationQuartuccio, John, Kristin Giammarco, and Mikhail Auguston. "Deriving stochastic properties from behavior models defined by Monterey Phoenix." System of Systems Engineering Conference (SoSE), 2017 12th. IEEE, 2017.en_US
dc.identifier.urihttp://hdl.handle.net/10945/59276
dc.descriptionThe article of record as published may be found a http://doi.org/10.1109/SYSOSE.2017.7994961
dc.descriptionPublished in: 2017 12th System of Systems Engineering Conference (SoSE); Date of Conference: 18-21 June 2017
dc.description.abstractStochastic properties of behavior models are of interest to the developer of a System of Systems (SoS) in order to gain insight to the likelihood of potential outcomes of the system. Constraints added to the system introduce changes to the inherent dependencies within a representative Bayesian belief network; thereby impacting the system. This paper defines a probability process model that may be used to identify the probability of outcomes compliant with behavior models defined in Monterey Phoenix (MP), with constraints added to the model.en_US
dc.format.extent6 p.en_US
dc.publisherIEEEen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleDeriving Stochastic Properties from Behavior Models Defined by Monterey Phoenixen_US
dc.typeArticleen_US
dc.contributor.corporateNaval Postgraduate School (U.S.)en_US
dc.contributor.departmentSystems Engineering (SE)
dc.contributor.departmentComputer Science (CS)
dc.subject.authorbehavior modelen_US
dc.subject.authorBayesian belief networken_US
dc.subject.authormodel checkingen_US
dc.subject.authorsystem of systemsen_US
dc.subject.authorlightweight formal methodsen_US


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