講演者
Prof Hakan Hjalmarsson（KTH（スウェーデン王立工科大学））
講演日時
2019年1月18日（金）　16:30～17:30
講演場所
創想館2階セミナールーム3（14-203）
講演概要
In this paper, we present the class of uncertain-input models, and extend it to handle cases of measurements with outliers. The general uncertain-input model framework allows us to treat system identification problems in which a linear system, represented by its impulse response, is subject to an input about which we have partial information. Both the impulse response and the input are modeled as Gaussian processes and the kernels are used to encode the information available. The whole model is then estimated using an approximate empirical Bayes approach. We extend the uncertain-input model framework to non-Gaussian measurement models by considering the noise precisions as realizations of a Gamma prior. This is joint work with Dr Riccardo Risuleo.
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