Robust Sequential Learning Algorithms for Linear Observation Models

@article{Deng2007RobustSL,
  title={Robust Sequential Learning Algorithms for Linear Observation Models},
  author={G. Deng},
  journal={IEEE Transactions on Signal Processing},
  year={2007},
  volume={55},
  pages={2472-2485}
}
  • G. Deng
  • Published 2007
  • Mathematics, Computer Science
  • IEEE Transactions on Signal Processing
  • This paper presents a study of sequential parameter estimation based on a linear non-Gaussian observation model. To develop robust algorithms, we consider a family of heavy-tailed distributions that can be expressed as the scale mixture of Gaussian and extend the development to include some robust penalty functions. We treat the problem as a Bayesian learning problem and develop an iterative algorithm by using the Laplace approximation for the posterior and the minorization-maximization (MM… CONTINUE READING
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