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    Static models, recursive estimators and the zero-variance approach

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    Type
    Presentation
    Authors
    Rubino, Gerardo
    Date
    2016-01-07
    Permanent link to this record
    http://hdl.handle.net/10754/624860
    
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    Abstract
    When evaluating dependability aspects of complex systems, most models belong to the static world, where time is not an explicit variable. These models suffer from the same problems than dynamic ones (stochastic processes), such as the frequent combinatorial explosion of the state spaces. In the Monte Carlo domain, on of the most significant difficulties is the rare event situation. In this talk, we describe this context and a recent technique that appears to be at the top performance level in the area, where we combined ideas that lead to very fast estimation procedures with another approach called zero-variance approximation. Both ideas produced a very efficient method that has the right theoretical property concerning robustness, the Bounded Relative Error one. Some examples illustrate the results.
    Conference/Event name
    Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
    Additional Links
    http://mediasite.kaust.edu.sa/Mediasite/Play/357c86cd3985448fad68293d3845ac531d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
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    Conference on Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)

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