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Sparse Bayesian Nonlinear System Identification using Variational Inference

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Figure

Fig. 1.Probabilistic graphical model of the hierarchical model represented in(14). The plate (box), denoted by the number of data samples N, indicates Ni.i.d observations
Fig. 2.Variational inference is performed by iteratively updating the VLB via an optimisation step: the diagram illustrates the variational Bayesian updateaccording to (30).
Fig. 3.Algorithmic description of the parameter estimation procedure forthe NARX model using variational Bayesian inference, termed VBNARX
Fig. 4.Sparse Bayesian identification of the NARX model using variationalinference and automatic relevance determination
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