Corpus ID: 209832221

Pearson chi^2-divergence Approach to Gaussian Mixture Reduction and its Application to Gaussian-sum Filter and Smoother

@article{Kitagawa2020PearsonCA,
  title={Pearson chi^2-divergence Approach to Gaussian Mixture Reduction and its Application to Gaussian-sum Filter and Smoother},
  author={Genshiro Kitagawa},
  journal={arXiv: Methodology},
  year={2020}
}
  • G. Kitagawa
  • Published 3 January 2020
  • Mathematics
  • arXiv: Methodology
The Gaussian mixture distribution is important in various statistical problems. In particular it is used in the Gaussian-sum filter and smoother for linear state-space model with non-Gaussian noise inputs. However, for this method to be practical, an efficient method of reducing the number of Gaussian components is necessary. In this paper, we show that a closed form expression of Pearson chi^2-divergence can be obtained and it can apply to the determination of the pair of two Gaussian… Expand

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