Synthesis of Knowledge for Information Oriented Society
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Photo Genta Ueno
The Institute of Statistical Mathematics, Research Organization of Information and Systems, Department of Statistical Modeling, Associate professor
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Data assimilation for the next generation: Automatic modeling and information detection
Finished by March 31, 2013

To acquire knowledge from large data, it is crucial to construct an appropriate model and understand a flow of information. It is, however, easier said than done. This study systematically develops methods for (1) making the model more efficient and less computationally expensive and (2) detecting a flow of information that is grasped by the model. These methods are based on the technique so-called "data assimilation," which synthesizes a model and data. The methods enable us to remodel the original model without the model developer and to conduct a dynamic analysis of the model output that may even exceed the amount of input data.