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Uncertainty analysis for complex systems modelled by computer simulators

Presented by: 
Michael Goldstein Durham University
Date: 
Tuesday 9th January 2018 - 09:00 to 10:00
Venue: 
INI Seminar Room 1
Abstract: 
This talk will present an overview of aspects of a general Bayesian methodology for performing detailed uncertainty analyses for complex physical systems which are modelled by computer simulators. Key features of this methodology are (i) simulator emulation, to allow us to explore the full range of outputs of the simulator (ii) history matching to identify all input choices consistent with historical data, and thus all future outcomes consistent with these choices (iii) structural discrepancy modelling, to make reliable uncertainty statements about the real world.
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Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons