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We study how to perform model selection for time series data where millions of candidate ARMA models may be eligible for selection. We propose a feasible computing method based on the Gibbs sampler. By this method model selection is performed through a random sample generation algorithm, and given a model of fixed dimension the parameter estimation is done(More)
In this paper, conventional D-S evidence theory (DST) is improved to address the problem of counter-intuitive behaviour in the application of D-S evidence theory. Firstly, the Dempster's rule of combination is extended to the general cases based on Xu Ling-yu's combination rule. Secondly, since all evidence resources are assumed fully reliable in evidence(More)
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