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Model-Data Integration in Physical SystemsEpisode from the podcastModel-Data Integration in Physical Systems
Multi model mixture density estimators & information theory for stochastic filtering and prediction

Multi model mixture density estimators & information theory for stochastic filtering and prediction

Released Friday, 21st March 2014
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Multi model mixture density estimators & information theory for stochastic filtering and prediction

Multi model mixture density estimators & information theory for stochastic filtering and prediction

Multi model mixture density estimators & information theory for stochastic filtering and prediction

Multi model mixture density estimators & information theory for stochastic filtering and prediction

Friday, 21st March 2014
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Branicki, M (University of Edinburgh)Tuesday 18 March 2014, 16:50-17:30

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The workshop is concerned with the subject of blending observational data with complex models, primarily in the physical sciences, with the aim of making improved predictions. The increasing complexity of phenomena that scientists and engineers wish to model, together with our increased ability to gather, store, and interrogate data, mean that the subjects of applied mathematics and statistics are increasingly required to work in conjunction in order to significantly progress understanding.Applications are far reaching and include the atmospheric sciences, geophysics, chemistry and signal processing. The workshop will bring together experts reflecting applied, computational, methodological and theoretical aspects of the subject area.

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